CN113516774A - Rendering quality adjustment method and related equipment - Google Patents

Abstract

The present application provides a cloud rendering platform to obtain a rendering task; the cloud rendering platform receives a rendering resource request instruction sent by a user equipment; the cloud rendering platform allocates computing resources to the rendering task according to the rendering resource request instruction to perform rendering to obtain a first rendered image; the cloud rendering platform receives a rendering resource adjustment instruction sent by the user equipment; the cloud rendering platform adjusts the computing resources allocated for the rendering task according to the rendering resource adjustment instruction; The cloud rendering platform uses the adjusted computing resources allocated for the rendering task to render the rendering task, thereby obtaining a second rendered image. The above solution can adjust the rendering quality in real time as needed to meet the different needs of users.

Description

Rendering quality adjustment method and related equipment

technical field

The present application relates to image rendering, and in particular, to a rendering quality adjustment method and related devices.

Background technique

Rendering refers to the process of using software to generate an image from a model, where a model is a description of a three-dimensional object in a strictly defined language or data structure, including geometry, viewpoint, texture, and lighting information. The images are digital images or bitmap images. Rendering is a term similar to "an artist's rendering of a scene", and is also used to describe "the process of computing effects in a video editing file to produce the final video output". Rendering can include pre-rendering/offline rendering or real-time rendering/onlinerendering, where pre-rendering is usually used for real-world simulation with predetermined scripts such as movies and advertisements; real-time rendering is usually used for Unscripted live-action simulation of flight training, 3D games, and interactive architectural demonstrations.

Real-time rendering requires very high computing speed. Taking a 3D game as an example, for a refresh rate of 30 frames per second, the time of each frame of image is about 33 milliseconds, so the rendering time of each frame of image must not exceed 33 milliseconds. In order to meet the computational speed requirements of real-time rendering, real-time rendering usually adopts rasterization rendering. The rasterization rendering adopts the design of the rendering pipeline (Render-pipeline). Once the rendering pipeline is started, the entire processing process must be coherent. To improve the image quality, the original rendering pipeline must be interrupted and restarted. The new rendering pipeline, therefore, makes it difficult to make adjustments to the rendering quality.

SUMMARY OF THE INVENTION

In order to solve the above problems, the present application provides a rendering quality adjustment method and related devices, which can adjust rendering quality in real time as required.

In a first aspect, a rendering quality adjustment method is provided, including:

The cloud rendering platform obtains rendering tasks;

The cloud rendering platform receives a rendering resource request instruction sent by the user equipment;

The cloud rendering platform allocates computing resources to the rendering task for rendering according to the rendering resource request instruction, thereby obtaining a first rendered image;

receiving, by the cloud rendering platform, a rendering resource adjustment instruction sent by the user equipment;

The cloud rendering platform adjusts the computing resources allocated for the rendering task according to the rendering resource adjustment instruction;

The cloud rendering platform uses the adjusted computing resources allocated for the rendering task to render the rendering task, thereby obtaining a second rendered image.

In some possible designs, when the rendering resource adjustment instruction is used to improve the rendering quality of the rendering task, the cloud rendering platform adjusts the computing resources allocated for the rendering task according to the rendering resource adjustment instruction Including: increasing the computing resources allocated for the rendering task; or in the case that the rendering resource adjustment instruction is used to reduce the rendering quality of the rendering task, the cloud rendering platform is adjusted according to the rendering resource adjustment instruction. The computing resources allocated by the rendering task include: reducing the computing resources allocated for the rendering task.

In some possible designs, after the cloud rendering platform allocates computing resources to the rendering task for rendering according to the rendering resource request instruction, thereby obtaining the first rendered image, the method further includes:

Calculate first pricing information according to the computing resources allocated to the rendering task, where the first pricing information is a fee for rendering the first rendered image;

After the cloud rendering platform uses the adjusted computing resources allocated for the rendering task to render the rendering task, so as to obtain a second rendered image, the method further includes:

Calculate second pricing information according to the adjusted computing resources allocated to the rendering task, where the second pricing information is a fee for rendering the second rendered image.

In some possible designs, ray tracing rendering is used to render the rendering task, and the rendering resource request instruction includes one or more of rendering metrics or resource parameters; wherein the rendering metrics include the number of samples per pixel One or more of Spp, the number of ray bounces, the number of object modeling triangles, the number of vertices, screen noise, and frame rate. The resource parameters include the number of processors, the main frequency of the processor, and the size of memory. and one or more of network bandwidth.

In some possible designs, the rendering task is sent by the user equipment, or sent by a management device.

In a second aspect, a cloud rendering platform is provided, including: an acquisition module, a receiving module, and a rendering engine;

The obtaining module is used to obtain rendering tasks;

The receiving module is configured to receive a rendering resource request instruction sent by the user equipment;

The rendering engine is configured to allocate computing resources to the rendering task for rendering according to the rendering resource request instruction, thereby obtaining a first rendered image;

The receiving module is configured to receive a rendering resource adjustment instruction sent by the user equipment;

The rendering engine is configured to adjust the computing resources allocated for the rendering task according to the rendering resource adjustment instruction;

The rendering engine is configured to render the rendering task using the adjusted computing resources allocated for the rendering task, thereby obtaining a second rendered image.

In some possible designs, when the rendering resource adjustment instruction is used to improve the rendering quality of the rendering task, the cloud rendering platform adjusts the computing resources allocated for the rendering task according to the rendering resource adjustment instruction Including: increasing the computing resources allocated for the rendering task; or in the case that the rendering resource adjustment instruction is used to reduce the rendering quality of the rendering task, the cloud rendering platform is adjusted according to the rendering resource adjustment instruction. The computing resources allocated by the rendering task include: reducing the computing resources allocated for the rendering task.

In some possible designs, the cloud rendering platform further includes a pricing module, and the pricing module is configured to calculate first pricing information according to allocating computing resources to the rendering task, wherein the first pricing information is for rendering the rendering task. the cost of the first rendered image;

The pricing module is further configured to calculate second pricing information according to the adjusted computing resources allocated to the rendering task, where the second pricing information is a fee for rendering the second rendered image.

In some possible designs, ray tracing rendering is used to render the rendering task, and the rendering resource request instruction includes one or more of rendering indicators, resource parameters, and display parameters; wherein, the rendering indicators include each One or more of the number of pixel samples Spp, the number of ray bounces, the number of object modeling triangles, the number of vertices, and screen noise. The resource parameters include the number of processors, the main frequency of the processor, and the memory size. and one or more of network bandwidth, the display parameters include frame rate.

In some possible designs, the rendering task is sent by the user equipment, or sent by a management device.

In a third aspect, a computer-readable storage medium is provided, comprising instructions that, when executed on the computer, cause the computer to perform the method according to any one of the first aspects.

In a fourth aspect, a computer program product is provided, when the computer program product is read and executed by the computer, the method according to any one of the first aspects will be executed.

In a fifth aspect, a rendering platform is provided, the rendering platform includes at least one rendering node, each rendering node includes a processor and a memory, and the processor executes a program in the memory, thereby executing any of the methods described in the first aspect. one of the methods described.

In the above solution, when the user equipment sends the rendering resource adjustment instruction, the cloud rendering platform adjusts the computing resources in real time according to the rendering resource adjustment instruction for calculation, thereby dynamically adjusting the rendering quality and adapting to the different needs of the user.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the accompanying drawings required in the embodiments or the background technology of the present application will be described below.

1A to 1B are schematic diagrams of some embodiments of ray tracing rendering in a simple scene involved in the present application;

2 is a schematic diagram of ray tracing rendering in a complex scene according to an embodiment of the present application;

3 is a schematic diagram of a sawtooth effect when the number of SPPs involved in the present application is relatively low;

4 is a schematic diagram of overcoming the sawtooth effect when the number of SPPs involved in the application is relatively high;

5A to 5A are schematic diagrams of no focal astigmatism spots formed when the number of SPPs involved in the present application is relatively low;

5B is a schematic diagram of the formation of focal astigmatism when the number of SPPs involved in the present application is relatively high;

6 is a schematic structural diagram of a cloud rendering system according to an embodiment provided by the present application;

7A to 7B are schematic diagrams of improving and reducing the rendering quality of a rendered image in real time by an intelligent terminal by pressing a button;

8A to 8B are schematic diagrams showing that the computer improves and reduces the rendering quality in real time by pressing buttons;

9A to 9B are schematic diagrams of the VR device improving and reducing the rendering quality in real time by pressing buttons;

10A to 10B are schematic diagrams illustrating the mapping relationship between the number of times of pressing the button and the adjustment index, respectively;

11A to 11D are schematic diagrams of some embodiments of adjusting computing resources for ray tracing rendering when rendering indicators are adjusted;

FIG. 12 is a flowchart interaction diagram of a rendering quality adjustment method according to an embodiment of the present application;

FIG. 13 is a flowchart interaction diagram of a rendering quality adjustment method according to another embodiment of the present application;

14 is a flowchart interaction diagram of a rendering quality adjustment method according to another embodiment of the present application;

15 is a schematic structural diagram of a cloud rendering platform proposed by the present application;

16 is a structural block diagram of an intelligent terminal of an implementation manner proposed by the present application;

17 is a structural block diagram of a computer of an implementation proposed by the present application;

FIG. 18 is a structural block diagram of a cloud rendering platform of an implementation manner proposed by the present application.

Detailed ways

Ray tracing rendering is a rendering method that produces a rendered image by tracing the path of light incident on a model along the rays emitted from the camera's (or human eye's) viewpoint toward each pixel of the rendered image. The core idea of ray tracing rendering is to trace rays backward from the viewpoint of the camera (or human eye). Since only the rays that finally enter the camera (or the human eye) are useful, backtracing rays can effectively reduce the amount of data. Rays in ray tracing rendering mainly include reflection and refraction, which will be described below with reference to specific embodiments.

In the reflection scene shown in FIG. 1A , the model has only one light source 130 and one opaque sphere 140 . A ray is emitted from the viewpoint E of the camera 110, projected to a pixel point O ₁ in the rendered image 120, and then continues to be emitted to a point P ₁ of the opaque sphere 140, and then reflected to the light source L, at this time, the point P The color of ₁ determines the color of pixel O ₁ . Another ray from the viewpoint E of the camera 110 is projected to another pixel point O ₂ in the rendered image 120 , and then continues to a point P ₂ of the opaque sphere 140 , which is then reflected to the light source L, and the point There is an obstacle opaque sphere 140 between P ₂ and the light source L. At this time, the point P ₂ is located in the shadow of the opaque sphere 140, and the color of the pixel point O ₂ is black.

In the refraction scene shown in FIG. 1B , the model has only one light source 230 and one transparent sphere 240 . A ray is emitted from the viewpoint E of the camera 210 and projected to a pixel point O ₃ in the rendered image 220 , and then continues to be emitted to a point P ₃ of the transparent sphere 240 , and then is refracted to the light source L. At this time, the point P The color of ₃ determines the color of pixel O ₃ .

It can be understood that the reflection scene in Fig. 1A and the refraction scene in Fig. 1B are the simplest scenes. Fig. 1A assumes that there is only one opaque sphere in the scene, and Fig. 1B assumes that there is only one transparent sphere in the scene. However, In practical applications, the scene is far more complex than that shown in Figure 1A and Figure 1B. For example, there may be multiple opaque objects and multiple transparent objects in the scene at the same time. Therefore, light rays will be reflected and refracted multiple times, resulting in ray tracing. becomes very complicated.

In the complex scene shown in FIG. 2 , the model includes a light source 330 , two transparent spheres 340 and 350 and an opaque object 360 . A ray is emitted from the viewpoint E of the camera 310, projected to a pixel point O ₄ in the rendered image 320, and continues to be emitted to a point P ₁ of the transparent sphere 340, and a shadow test line S1 is drawn from P ₁ to the light source L, during which There is no occluded object, then, the light intensity of the light source pair P ₁ in the direction of its line of sight E can be calculated by using the local light illumination model, as the local light intensity of this point. At the same time, the reflected ray R1 and the refracted ray T1 at this point are also tracked, which also contribute to the light intensity at point _P1 . In the direction of the reflected ray R1, if it does not intersect with other objects, then the light intensity in this direction is set to zero, and the tracking of this ray direction is ended. Then, continue to track the direction of the refracted ray T1 to calculate the light intensity contribution of the ray. _The refracted ray T1 propagates inside the transparent object 340, and continues to exit and intersects with the transparent object 350 at the point P2. Since this point is inside the transparent object 350, it can be assumed that its local light intensity is zero, and at the same time, the reflected ray R2 and the refracted light are generated. Ray T2, in the direction of reflected ray R2, can continue to recursively track to calculate its light intensity, and it will not continue here. Continue to track the refracted ray T2. T2 and the opaque object 360 intersect at point P ₃ , and make the shadow test line S3 between P ₃ and the light source L. If there is no object blocking, then calculate the local light intensity there. Since the opaque object 360 is non-transparent , then, you can continue to track the light intensity in the direction of the reflected ray R3, and combine the local light intensity to obtain the light intensity at _P3 . The tracking of the reflected ray R3 is similar to the previous process, and the algorithm can proceed recursively. Repeat the above process until the ray meets the trace termination condition. In this way, we can get the light intensity of pixel O ₄ , which is its corresponding color value.

It can be understood that the complex scene shown in Figure 2 above is only one of the complex scenes. In other complex scenes, the number of light sources in the model, the number of transparent objects, the number of opaque objects, the position of the camera, the position of the light source , the position of the transparent object and the position of the opaque object can all be changed, which are not specifically limited here.

The rendering quality of ray-traced rendering can depend on the following rendering metrics, resource parameters, and display parameters. in,

Rendering metrics can include the number of samples per pixel (Sample per pixel, Spp), the number of ray bounces (Bounce), the number of object modeling triangles, the number of vertices, and screen noise. The following will take Spp and Bounce as examples for detailed introduction.

SPP can be defined as the number of rays sampled per pixel. The reason why the number of Spp can affect the quality of the rendered image is that if Spp is 1 (that is, only one ray per pixel passes through), even if the light is slightly shifted, the color of the pixel may change greatly. In the following examples, it is assumed that the light will eventually be reflected to the light source, but, for the sake of brevity, it will not be stated in the following. Taking the example shown in Figure 3, if the light passes through the pixel point A, the light will be projected on the red opaque object 1. At this time, the color value of the pixel point A is determined by the projection point on the opaque object 1. , that is, the color of pixel A is red. If the light passes through the pixel point B, the light will be projected on the green opaque object 2. At this time, the color value of the pixel point B is determined by the projection point on the opaque object 2, that is, the color of the pixel point B. for green. Therefore, although the pixel point A and the pixel point B are adjacent pixels, the colors of the pixel point A and the pixel point B will be very different, resulting in a sawtooth effect. In order to solve the above problem, taking the example shown in Figure 4, if Spp is n (that is, n rays are emitted from the viewpoint to the same pixel on the rendered image), then these n rays pass through the pixels and are projected on the opaque object. 1 or the n projection points of the opaque object 2, so that the n color values of the pixel can be determined according to the n projection points, and finally, the n color values are averaged to obtain the final color value of the pixel. If the final color value of the pixel conforms to the picture reference frame (mathematical expectation), the sampling noise is lower. Therefore, the higher the number of Spp, the better the antialiasing of the rendered image, the lower the noise index, the better the quality of the rendered image. In addition, the number of SPPs can affect the light effects of the picture, such as the caustic effects (Caustics) formed by transparent bodies (glass balls, water ripples) under the illumination of light. When the number of samples is small, as shown in FIG. 5A , the first ray is emitted from the viewpoint E of the camera 410 , passes through a pixel point O ₁ of the rendered image 420 , exits to a point P ₁ of the transparent object 440 , and is refracted to the light point H ₁ ; a second ray is emitted from the viewpoint E of the camera 410 , passes through a pixel point O ₂ of the rendered image 420 , exits to a point P ₂ of the transparent object 440 , and is refracted to the light point H ₂ ; The viewpoint E of the camera 410 emits a third ray, which passes through a pixel point O ₃ of the rendered image 420 , exits to a point P ₃ of the transparent object 440 , and is refracted to the light point H ₃ , the light point H ₁ , and the light point H ₂ and the light spot H ₃ are just isolated light spots and cannot be gathered into a focal spot. When the number of samples increases, as shown in FIG. 5B , the first ray is emitted from the viewpoint E of the camera 410 , passes through a pixel point O ₁ of the rendered image 420 , exits to a point P ₁ of the transparent object 440 , and is refracted to the light point H ₁ ; a second ray is emitted from the viewpoint E of the camera 410 , passes through a pixel point O ₂ of the rendered image 420 , exits to a point P ₂ of the transparent object 440 , and is refracted to the light point H ₂ ; The viewpoint E of the camera 410 emits a third ray, which passes through a pixel point O ₃ of the rendered image 420 , exits to a point P ₃ of the transparent object 440 , and is refracted to the light point H ₃ ; Four rays, through a pixel point O ₄ of the rendered image 420 , exit to a point P ₄ of the transparent object 440 , and are refracted to the light point H ₄ ; a fifth ray is emitted from the viewpoint E of the camera 410 and passes through the rendered image A pixel point O ₅ of 420 exits to a point P ₅ of the transparent object 440 and is refracted to a light point H ₅ , wherein the light point H ₁ and the light point H ₄ form the focal astigmatism spot G1 (estimated value), and the light The point _H3 and the light point _H5 constitute the focal astigmatism spot G2 (estimated value). Therefore, if the SPP value is low, the visual effects of caustics cannot be effectively generated, and other rendering methods (non-ray tracing methods) are often used to add this visual effect.

The number of ray bounces is the sum of the maximum number of reflections and refractions that can be traced to the ray before the ray trace is terminated. The reason why the number of ray bounces can affect the image quality of the rendered image is because in a complex scene, the light will be reflected and refracted many times. In theory, the number of times the light will be reflected and refracted can be infinite. In the actual process of the algorithm, it is impossible to trace the rays infinitely, so some termination conditions of the trace need to be given. In the application, there can be the following termination conditions: the light will decay after many times of reflection and refraction, and the light contribution to the light intensity of the viewpoint is very small; the number of ray bounces, that is, the tracking depth is greater than a certain value. Therefore, the more times the light bounces, the more effective light rays can be tracked, the better the refraction effect between multiple transparent objects, the more realistic, and the better the image quality.

The resource parameter may include one or more of the number of processors, the main frequency of the processor, the size of the memory, and the network bandwidth. When the main frequency of the processor is higher, the rendering efficiency is higher, and the frame rate of the image is higher; when the memory is larger, the rendering efficiency is higher, and the frame rate of the image is higher; when the network bandwidth is larger, the efficiency of transmitting the rendered image is higher. The higher it is, the higher the frame rate at which the rendered image is displayed on the user device.

The display parameters may include: frame rate, etc. The higher the display parameters, the higher the user experience.

A rendering task, such as an application or a movie, consists of multiple frames of rendered images. The rendering quality of a rendering task includes at least one of the following two aspects: the quality of the rendered image in each frame, and the efficiency of the rendered image (ie, the frame rate).

It can be seen from the above records that the rendering quality of the rendering task can be adjusted by adjusting the rendering indicators and resource parameters.

The higher the rendering index, for example, the larger the number of Spp, the more times the light bounces, and the higher the quality of the rendered image per frame, the better the rendering quality. Generally, a higher rendering index also leads to a larger amount of computation required for ray tracing rendering, and accordingly, more resources (computing resources, storage resources, network resources, etc.) are required.

The higher the display parameters, the higher the rendering quality and the more resources (computing resources, storage resources, network resources, etc.) required.

The higher the resource parameter, the higher the efficiency of rendering the image, the higher the image frame rate, and the better the rendering quality.

Therefore, the resources required for ray tracing rendering can be provided on demand through the cloud rendering platform, thereby providing rendering quality suitable for user needs on demand.

Referring to FIG. 6, FIG. 6 is a schematic structural diagram of a cloud rendering system provided by the present application. In the specific implementation manner of the present application, a cloud rendering system can be used to implement ray tracing rendering, including: a user device 510 , a network device 520 , and a cloud rendering platform 530 .

The user equipment 510 may be a device that needs to display rendered images in real time, for example, a virtual reality device (Virtual Reality, VR) for flight training, a computer for 3D games, and a smartphone for interactive architectural demonstrations etc., which are not specifically limited here.

The network device 520 is used to transmit data between the user equipment 510 and the cloud rendering platform 530 through a communication network of any communication mechanism/communication standard. The communication network may be a wide area network, a local area network, a point-to-point connection, etc., or any combination thereof.

The cloud rendering platform 530 includes a plurality of rendering nodes, and each rendering node includes rendering hardware, a virtual machine, an operating system, a rendering engine, and a rendering application from bottom to top. The rendering hardware includes computing resources, storage resources, and network resources. The computing resources may adopt a heterogeneous computing architecture, for example, a central processing unit (Central Processing Unit, CPU) + graphics processing unit (Graphics Processing Unit, GPU) architecture, CPU+AI chip, CPU+GPU+AI chip architecture, etc. There is no specific limitation here. Storage resources can include memory and so on. Here, computing resources may be divided into multiple computing unit resources, storage resources may be divided into multiple storage unit resources, and network resources may be divided into multiple network unit resources. Therefore, the cloud rendering platform can be freely combined on the basis of unit resources according to the user's resource requirements, so as to provide resources according to the user's needs. For example, computing resources can be divided into 5u computing unit resources, and storage resources can be divided into 10G storage unit resources, then the combination of computing resources and storage resources can be, 5u+10G, 5u+20G, 5u+30u, ..., 10u +10G, 10u+20G, 10u+30u, …. Virtualization service is a service that builds the resources of multiple physical hosts into a unified resource pool through virtualization technology, and flexibly isolates independent resources according to the needs of users to run the user's application program. Generally, the virtualization service may include a virtual machine (Virtual Machine, VM) service, a bare metal server (Bare Metal Server, BMS) service, and a container (Container) service. The VM service may be a service that virtualizes a virtual machine (Virtual Machine, VM) resource pool on multiple physical hosts by using a virtualization technology to provide a user with a VM for use on demand. The BMS service is a service that virtualizes a BMS resource pool on multiple physical hosts to provide users with a BMS for use on demand. Container service is a service that virtualizes container resource pools on multiple physical hosts to provide users with containers for use on demand. A VM is a simulated virtual computer, that is, a logical computer. BMS is an elastically scalable high-performance computing service. Its computing performance is the same as that of traditional physical machines, and it has the characteristics of safe physical isolation. Containers are a kernel virtualization technology that provides lightweight virtualization to isolate user space, processes, and resources. It should be understood that the VM services, BMS services and container services in the above-mentioned virtualization services are only specific examples. In practical applications, the virtualization services can also be other lightweight or heavyweight virtualization services, which are not described here. limited. The rendering engine may be a ray tracing renderer for implementing ray tracing rendering algorithms. Commonly used ray tracing renderers may include Unity, V-ray, Unreal, RenderMan, and so on. The rendering application can be used to call the rendering engine to complete the rendering of the rendered image. Common rendering applications can include: game applications, VR applications, movie special effects, animations, and so on.

The following Figures 7A to 7B, 8A to 8B, and 9A to 9B respectively take the application scenarios in which the user equipment is a smart terminal, a computer, and a VR device as examples, and describe in detail how the user can render the rendered image in real time by pressing buttons. Quality adjustment is described in detail.

FIGS. 7A to 7B take the user equipment as an intelligent terminal as an example, and describe how the user adjusts the rendering index, thereby adjusting the rendering quality of the rendered image. The smart terminal is displaying the rendered image in real time. The side of the smart terminal is provided with two buttons, the increase button and the decrease button. The user can improve the rendering quality or reduce the rendering quality by pressing the increase button and the decrease button. Specifically, as shown in FIG. 7A , when the user wants to improve the rendering quality, he can press the add button on the side of the smart terminal. The more times the add button of the smart terminal is pressed by the user, the higher the rendering quality is improved. As shown in FIG. 7B , when the user wants to reduce the rendering quality, he can press the reduce button on the side of the smart terminal. The more times the reduce button of the smart terminal is pressed by the user, the higher the rendering quality will be degraded. In the above embodiment, the increase button and the decrease button are used to adjust the rendering quality, and the increase button and the decrease button are two different physical buttons. However, in practical applications, the increase button and the decrease button can be integrated into the same physical button. Alternatively, the increase button and the decrease button may be two different virtual buttons, and even more, the components such as the increase button and the decrease button may not be used, but other components such as a progress bar may be used, which are not specifically limited here.

FIGS. 8A to 8B take the user equipment as a computer as an example, and describe how the user adjusts the rendering index to adjust the rendering quality. The smart terminal is displaying the rendered image in real time. The user can set the left button of the mouse to increase the button, and the right button of the mouse to decrease the button. The user can increase the rendering quality or decrease the rendering quality by pressing the left mouse button and the right mouse button. Specifically, as shown in FIG. 8A , when the user wants to improve the rendering quality, he can press the left button of the mouse. The more times the left button of the mouse of the terminal device is pressed by the user, the higher the rendering quality is improved. As shown in FIG. 8B , when the user wishes to reduce the rendering quality, the user can press the right button of the mouse, and the more times the right button of the mouse is pressed by the user, the higher the rendering quality decreases. In the above embodiment, the rendering quality is adjusted through the left and right keys of the mouse. In other embodiments, the rendering quality can also be adjusted through the up and down keys of the keyboard, or the rendering quality can be adjusted through the pulley of the mouse, etc. There is no specific limitation here.

FIGS. 9A to 9B take the user device as a VR device as an example, and describe how the user adjusts the rendering index to adjust the rendering quality. The side of the VR device is provided with two buttons, the increase button and the decrease button. Users can improve the rendering quality or reduce the rendering quality by pressing the increase button and the decrease button. Specifically, as shown in FIG. 9A , when the user wants to improve the rendering quality, he can press the add button on the side of the VR device. The more times the add button of the VR device is pressed by the user, the higher the rendering quality is improved. As shown in FIG. 9B , when the user wants to reduce the rendering quality, he can press the decrease button on the side of the VR device. The more times the decrease button of the VR device is pressed by the user, the higher the rendering quality is degraded. In the above embodiment, the increase button and the decrease button are used to adjust the rendering quality, and the increase button and the decrease button are two different physical buttons. However, in practical applications, the increase button and the decrease button can be integrated into the same physical button. above, there is no specific limitation here.

It should be understood that the above application scenarios of the smart terminal, the computer, and the VR device are only specific examples, and in other embodiments, other application scenarios may also be used, which are not specifically limited here.

The real-time adjustment of the rendering quality by the user by pressing the button on the user equipment mainly includes the following processes: (1) The user obtains the adjustment index and/or the adjustment parameter by pressing the button on the user equipment. (2) The user equipment sends the adjustment indicators and/or adjustment parameters to the cloud rendering platform through the network device. (3) The cloud rendering platform adjusts computing resources to perform ray tracing rendering according to adjustment indicators and/or adjustment parameters, thereby obtaining a rendered image. (4) The cloud rendering platform sends the rendered image to the user device through the network device, and presents it to the user.

In step (1), the user obtains the adjustment index and/or adjustment parameter by pressing a button on the user equipment. Among them, the mapping relationship between the number of times of pressing the increase button and the adjustment index (SPP, the number of times of light bounce) and/or the adjustment parameter (frequency and memory) can be preset in the user equipment, and the number of times of pressing the button to reduce and The mapping relationship between adjustment indicators (SPP, the number of bounces of light) and/or adjustment parameters (frequency and memory).

As shown in (a) of FIG. 10A , when the number of times the user presses the increase button is 1, the increase number of SPP is 4, and when the number of times the user presses the increase button is 2, the SPP increases to 8, and when the user presses When the number of times the increase button is pressed is 3, the SPP is increased to 12, and when the number of times the user presses the increase button is 4, the SPP is increased to 16. As shown in (b) of FIG. 10A , when the number of times the user presses the increase button is 1, the number of times the light bounces is increased to 1, and when the number of times the user presses the increase button is 2, the number of times the light bounces is increased to 2 , when the number of times the user presses the increase button is 3, the number of times the light bounces is increased to 3, and when the number of times the user presses the increase button is 4, the number of times the light bounces is increased to 4. As shown in (c) of FIG. 10A , when the number of times the user presses the increase button is 1, the main frequency of the processor increases to 8u, and when the number of times the user presses the increase button is 2, the main frequency of the processor increases is 16u. When the user presses the increase button for 3 times, the processor's main frequency increases to 24u. When the user presses the increase button for 4 times, the processor's main frequency increases to 32u. As shown in (d) in FIG. 10A , when the number of times the user presses the increase button is 1, the memory of the memory increases to 10 megabytes (M), and when the number of times the user presses the increase button is 2, the memory of the memory increases. When the number of times the user presses the increase button is 3, the memory of the memory increases to 30M, and when the number of times the user presses the increase button is 4, the memory of the memory increases to 40M.

As shown in (a) of FIG. 10B , when the number of times the user presses the decrease key is 1, the SPP is reduced to 16, and when the number of times the user presses the decrease key is 2, the SPP is reduced to 12, and when the user presses the decrease key, the SPP is reduced to 12. When the number of times of pressing the decrease button is 3, the SPP is reduced to 8, and when the number of times the user presses the decrease button is 4, the SPP is reduced to 4. As shown in (b) of FIG. 10B , when the number of times the user presses the decrease button is 1, the number of times the light bounces is reduced to 4, and when the number of times the user presses the decrease button is 2, the number of times the light bounces is reduced is 3, when the number of times the user presses the decrease button is 3, the SPP is decreased to 2, and when the number of times the user presses the decrease button is 4, the SPP is decreased to 1. As shown in (c) of FIG. 10B , when the number of times the user presses the decrease button is 1, the main frequency of the processor is reduced to 32u, and when the number of times the user presses the decrease button is 2, the main frequency of the processor is reduced is 24, when the number of times the user presses the decrease button is 3, the main frequency of the processor is reduced to 16, and when the number of times the user presses the decrease button is 4, the main frequency of the processor is reduced to 8. As shown in (d) in FIG. 10B , when the number of times the user presses the decrease button is 1, the memory of the memory is reduced to 40M, and when the number of times the user presses the decrease button is 2, the memory of the memory is reduced to 30M. When the number of times the user presses the decrease button is 3, the memory of the memory is reduced to 20M, and when the number of times the user presses the decrease button is 4, the memory of the memory is reduced to 10M.

In the above example, increase the number of button presses, decrease the number of button presses, increase the number of SPP, increase the number of light bounces, decrease the number of button presses, decrease the number of SPP, and decrease the number of light bounces only as Specific examples, in practical applications, may also be other numerical values, which are not specifically limited here. In addition, in the above example, the number of SPP and the number of light bounces are increased for each press of the button. In practical applications, the number of SPP can be increased first when the increase button is pressed for the first time. When the increase button is pressed a second time, the number of light bounces is increased again, and so on.

In step (3), the cloud rendering platform adjusts computing resources to perform ray tracing rendering according to the adjustment index, thereby obtaining a rendered image. The following will take the adjustment index as an improvement index, which requires additional computing resources for ray tracing rendering, and the adjustment index as a lower index, which requires reducing computing resources for ray tracing rendering.

For adjusting the index to improve the index, it is necessary to increase the computing resources for ray tracing rendering. Under the condition that the number of ray bounces remains the same and the SPP increases, as shown in Figure 11A, before the adjustment, assuming that the first rendering index is that the SPP is 1 (the number of rays passing through each pixel is 1), the cloud rendering platform uses the thread 1. Perform ray tracing on the ray 1 passing through the pixel A, wherein the computing resource used by the thread 1 is the first computing resource. When the user adjusts the first rendering index (SPP is 1) to the second rendering index (SPP is 2), that is, when the adjustment index is SPP increased by 1, the cloud rendering platform keeps the thread 1 to the light 1 passing through the pixel A Perform ray tracing, and the cloud rendering platform newly creates thread 2 to perform ray tracing on the newly added ray 2 passing through the pixel A, wherein the computing resources used by thread 2 are the newly added computing resources, and the second computing resources are equal to The sum of the first computing resource and the newly added computing resource. When the SPP remains unchanged and the number of ray bounces increases, as shown in Figure 11B , assuming that the first rendering index is that the number of ray bounces is 1, the cloud rendering platform uses thread 1 to adjust the number of ray 1 passing through the pixel A. The first bounce is used for ray tracing, wherein the computing resource used by thread 1 is the first computing resource. When the user adjusts the first rendering index (the number of ray bounces is 1) to the second rendering index (the number of ray bounces is 2), that is, when the adjustment index is the number of ray bounces increasing by 1, the cloud rendering platform keeps 1 pair of threads. The first bounce of the ray 1 passing through the pixel A performs ray tracing, and the newly created thread 2 of the cloud rendering platform performs ray tracing on the second bounce of the ray 1 passing through the pixel A, wherein the thread 2 The used computing resource is the newly added computing resource, and the second computing resource is equal to the sum of the first computing resource and the newly added computing resource.

For adjusting the index to reduce the index, it is necessary to reduce the computing resources for ray tracing rendering. Under the condition that the number of ray bounces remains the same and the SPP decreases, as shown in Figure 11C, before the adjustment, assuming that the first rendering index is SPP of 2 (the number of rays passing through each pixel is 2), the cloud rendering platform uses the thread 1. Perform ray tracing on the ray 1 passing through the pixel A, and perform ray tracing on the ray 2 passing through the pixel A through thread 2. The computing resource used by thread 1 is the first computing resource, and the The computing resource is the first computing resource. When the user adjusts the first rendering index (SPP is 2) to the second rendering index (SPP is 1), that is, when the adjustment index is SPP reduced by 1, the cloud rendering platform keeps the thread 1 to the light 1 passing through the pixel A Perform ray tracing, and the cloud rendering platform deletes thread 2 to perform ray tracing on the newly added ray 2 passing through the pixel A, wherein the computing resources used by thread 2 are the reduced computing resources, and the second computing resources are equal to the first. The difference between computing resources and reduced computing resources. In the case where the SPP remains unchanged and the number of light bounces decreases, as shown in Figure 11D , assuming that the first rendering index is that the number of ray bounces is 2, the cloud rendering platform uses thread 1 to adjust the number of ray 1 passing through the pixel A. The first bounce is used for ray tracing, and the second bounce of ray 1 passing through the pixel A is ray traced by thread 2. The computing resources used by thread 1 are the first computing resources and those used by thread 2 The computing resource is the second computing resource. When the user adjusts the first rendering index (the number of ray bounces is 2) to the second rendering index (the number of ray bounces is 1), that is, when the adjustment index is that the number of ray bounces is reduced by 1, the cloud rendering platform keeps 1 pair of threads. The first bounce of the ray 1 passing through the pixel A performs ray tracing, and the cloud rendering platform deletes the thread 2 to perform ray tracing on the second bounce of the ray 1 passing through the pixel A. The used computing resources are reduced computing resources, and the second computing resources are equal to the difference between the first computing resources and the reduced computing resources.

In the above example, the number of first rendering indicators, the number of second rendering indicators, and the number of adjustment indicators are only specific examples. In practical applications, other values may also be used, which are not specifically limited here. In addition, the above examples are all triggered by the user pressing a button. However, in practical applications, the user can also be a professional with a profound technical foundation, and can directly call the Application Programming Interface (API) to trigger, There is no specific limitation here.

The process of adjusting the rendering quality by the user through the cloud rendering system will be described in detail below. Referring to FIG. 12 , FIG. 12 is an interactive flowchart of a rendering quality adjustment method according to an embodiment of the present application. The rendering quality adjustment method in this embodiment may include the following steps:

S101: The cloud rendering platform receives the rendering task sent by the management device. Correspondingly, the management device receives the rendering task sent by the cloud rendering platform.

In a specific embodiment of the present application, the management device may be a third-party device. For example, the user equipment may be the equipment where the application program of the 3D game is played by the user, and the management equipment may be the equipment of the game developer who provides the application program of the 3D game.

S102: The user equipment obtains a rendering resource request instruction input by the user.

In a specific embodiment of the present application, the rendering resource request instruction includes one or more of rendering indicators, resource parameters, and display parameters.

In a specific embodiment of the present application, the rendering index includes one or more of the number of samples per pixel Spp, the number of ray bounces, the number of object modeling triangles, the number of vertices, and picture noise. Among them, SPP can be defined as the number of rays sampled by each pixel. The number of ray bounces is the sum of the maximum number of reflections and refractions that can be traced to the ray before the ray trace is terminated. Here, the higher the rendering index, for example, the greater the number of Spps, the more times the ray bounces, the greater the amount of computation required for ray tracing rendering, and accordingly, the required resources (computing resources, storage resources, and network resources, etc. etc.) will also be more, the better the rendering quality.

In a specific embodiment of the present application, the resource parameter includes one or more of the number of processors, the main frequency of the processor, the size of the memory, and the network bandwidth. Here, the higher the resource parameter, the more resources are allocated to the rendering task, the higher the frame rate of the rendered image, and the better the user experience.

In a specific embodiment of the present application, the display parameters may include frame rate and the like, and the higher the rendering quality, the more resources (computing resources, storage resources, network resources, etc.) are required.

S103: The user equipment sends the rendering resource request instruction to the cloud rendering platform through the network device. Correspondingly, the cloud rendering platform receives the rendering resource request instruction sent by the user equipment through the network device.

S104: The cloud rendering platform allocates computing resources to perform real-time rendering for the rendering task according to the rendering resource request instruction, thereby obtaining a first rendered image.

S105: The cloud rendering platform sends the first rendered image to the user equipment through the network device. Correspondingly, the user equipment receives the first rendered image sent by the cloud rendering platform through the network device.

In a specific embodiment of the present application, the cloud rendering platform calculates first pricing information according to allocating computing resources to the rendering task, where the first pricing information is a fee for rendering the first rendered image.

S106: The user equipment obtains the rendering resource adjustment instruction input by the user.

In a specific embodiment of the present application, the rendering resource adjustment instruction is to improve the rendering quality, or to reduce the rendering quality.

S107: The user equipment sends the rendering resource adjustment instruction to the cloud rendering platform through the network device.

S108: The cloud rendering platform adjusts the computing resources allocated for the rendering task according to the rendering resource adjustment instruction, and uses the adjusted computing resources allocated for the rendering task to render the rendering task, Thereby, a second rendered image is obtained.

In a specific embodiment of the present application, in the case that the rendering resource adjustment instruction is used to improve the rendering quality of the rendering task, the cloud rendering platform adjusts the amount allocated for the rendering task according to the rendering resource adjustment instruction The computing resources include: increasing the computing resources allocated for the rendering task; or when the rendering resource adjustment instruction is used to reduce the rendering quality of the rendering task, the cloud rendering platform adjusts according to the rendering resource adjustment instruction The computing resources allocated for the rendering task include: reducing the computing resources allocated for the rendering task.

S109: The cloud rendering platform sends the second rendered image to the user equipment through the network device.

In a specific embodiment of the present application, the cloud rendering platform calculates second pricing information according to the adjusted computing resources allocated to the rendering task, where the second pricing information is a fee for rendering the second rendered image.

In the above example, the application provider can first propose rendering tasks on the cloud rendering platform, and then each user device can adjust the rendering quality according to its own needs. For example, the application provider of a 3D game can propose rendering tasks first. The task renders the game scene of a 3D game. Some gamers are willing to pay to improve the rendering quality of the game scene. They can send rendering resource adjustment instructions to request the cloud rendering platform to provide more computing resources; If the image quality requirements are not high, you can send a rendering resource adjustment command to request the cloud rendering platform to reduce the provided computing resources and reduce costs.

For the sake of simplicity, the rendering quality adjustment method will not be introduced here. For details, please refer to FIGS. 1A-1B, 2-4, 5A-5B, 6, 7A-7B, 9A-9B, 10A-10B, 11A-11B and related descriptions.

The following will describe in detail the process of adjusting the rendering quality by the user through the cloud rendering system. Referring to FIG. 13 , FIG. 13 is an interactive flowchart of a rendering quality adjustment method according to another embodiment of the present application. The rendering quality adjustment method in this embodiment may include the following steps:

S201: The cloud rendering platform receives the rendering task sent by the user equipment. Correspondingly, the cloud rendering platform receives the rendering task sent by the user equipment.

In the specific embodiment of the present application, the rendering task may be the task of rendering multi-frame images proposed by the same application, or may be the task of rendering multi-frame images proposed by multiple applications respectively, which is not specifically described here. limited. For example, the user may have been playing game A, and then the rendering task may be a rendering task initiated by the application of game A. The user may also play the A game first and then play the B game. Then, the rendering task may be a rendering task initiated by the application of the A game and the application of the B game.

S202: The user equipment obtains a rendering resource request instruction input by the user.

In a specific embodiment of the present application, the rendering resource request instruction includes one or more of rendering indicators, resource parameters, and display parameters.

In a specific embodiment of the present application, the rendering index includes one or more of the number of samples per pixel Spp, the number of ray bounces, the number of object modeling triangles, the number of vertices, and picture noise. Among them, SPP can be defined as the number of rays sampled by each pixel. The number of ray bounces is the sum of the maximum number of reflections and refractions that can be traced to the ray before the ray trace is terminated. Here, the higher the rendering index, for example, the greater the number of Spps, the more times the ray bounces, the greater the amount of computation required for ray tracing rendering, and accordingly, the required resources (computing resources, storage resources, and network resources, etc. etc.) will also be more, the better the rendering quality.

In a specific embodiment of the present application, the resource parameter includes one or more of the number of processors, the main frequency of the processor, the size of the memory, and the network bandwidth. Here, the higher the resource parameter, the more resources are allocated to the rendering task, the higher the frame rate of the rendered image, and the better the user experience.

In a specific embodiment of the present application, the display parameters may include frame rate and the like, and the higher the rendering quality, the more resources (computing resources, storage resources, network resources, etc.) are required.

S203: The user equipment sends the rendering resource request instruction to the cloud rendering platform through the network device. Correspondingly, the cloud rendering platform receives the rendering resource request instruction sent by the user equipment through the network device.

S204: The cloud rendering platform allocates computing resources to perform real-time rendering for the rendering task according to the rendering resource request instruction, thereby obtaining a first rendered image.

S205: The cloud rendering platform sends the first rendered image to the user equipment through the network device. Correspondingly, the user equipment receives the first rendered image sent by the cloud rendering platform through the network device.

In a specific embodiment of the present application, the cloud rendering platform calculates first pricing information according to allocating computing resources to the rendering task, where the first pricing information is a fee for rendering the first rendered image.

S206: The user equipment obtains the rendering resource adjustment instruction input by the user.

In a specific embodiment of the present application, the rendering resource adjustment instruction is to improve the rendering quality, or to reduce the rendering quality.

S207: The user equipment sends the rendering resource adjustment instruction to the cloud rendering platform through the network device.

S208: The cloud rendering platform adjusts the computing resources allocated for the rendering task according to the rendering resource adjustment instruction, and uses the adjusted computing resources allocated for the rendering task to render the rendering task, Thereby, a second rendered image is obtained.

In a specific embodiment of the present application, in the case that the rendering resource adjustment instruction is used to improve the rendering quality of the rendering task, the cloud rendering platform adjusts the amount allocated for the rendering task according to the rendering resource adjustment instruction The computing resources include: increasing the computing resources allocated for the rendering task; or when the rendering resource adjustment instruction is used to reduce the rendering quality of the rendering task, the cloud rendering platform adjusts according to the rendering resource adjustment instruction The computing resources allocated for the rendering task include: reducing the computing resources allocated for the rendering task.

S209: The cloud rendering platform sends the second rendered image to the user equipment through the network device.

In a specific embodiment of the present application, the cloud rendering platform calculates second pricing information according to the adjusted computing resources allocated to the rendering task, where the second pricing information is a fee for rendering the second rendered image.

For the sake of simplicity, the rendering quality adjustment method will not be introduced here. For details, please refer to FIGS. 1A-1B, 2-4, 5A-5B, 6, 7A-7B, 9A-9B, 10A-10B, 11A-11B and related descriptions.

The following will describe in detail the process of adjusting the rendering quality by the user through the cloud rendering system. Referring to FIG. 14 , FIG. 14 is an interactive flowchart of a rendering quality adjustment method according to another embodiment of the present application. The rendering quality adjustment method in this embodiment may include the following steps:

S301: The cloud rendering platform receives the rendering task sent by the first user equipment. Correspondingly, the cloud rendering platform receives the rendering task sent by the first user equipment.

In the specific embodiment of the present application, the rendering task may be the task of rendering multi-frame images proposed by the same application, or may be the task of rendering multi-frame images proposed by multiple applications respectively, which is not specifically described here. limited. For example, the user may have been playing game A, and then the rendering task may be a rendering task initiated by the application of game A. The user may also play the A game first and then play the B game. Then, the rendering task may be a rendering task initiated by the application of the A game and the application of the B game.

S302: The first user equipment obtains a rendering resource request instruction input by the user.

In a specific embodiment of the present application, the rendering resource request instruction includes one or more of rendering indicators, resource parameters, and display parameters.

In a specific embodiment of the present application, the rendering index includes one or more of the number of samples per pixel Spp, the number of ray bounces, the number of object modeling triangles, the number of vertices, and picture noise. Among them, SPP can be defined as the number of rays sampled by each pixel. The number of ray bounces is the sum of the maximum number of reflections and refractions that can be traced to the ray before the ray trace is terminated. Here, the higher the rendering index, for example, the greater the number of Spps, the more times the ray bounces, the greater the amount of computation required for ray tracing rendering, and accordingly, the required resources (computing resources, storage resources, and network resources, etc. etc.) will also be more, the better the rendering quality.

In a specific embodiment of the present application, the resource parameter includes one or more of the number of processors, the main frequency of the processor, the size of the memory, and the network bandwidth. Here, the higher the resource parameter, the more resources are allocated to the rendering task, the higher the frame rate of the rendered image, and the better the user experience.

In a specific embodiment of the present application, the display parameters may include frame rate and the like, and the higher the rendering quality, the more resources (computing resources, storage resources, network resources, etc.) are required.

S303: The first user equipment sends the rendering resource request instruction to the cloud rendering platform through the network device. Correspondingly, the cloud rendering platform receives the rendering resource request instruction sent by the first user equipment through the network device.

S304: The cloud rendering platform allocates computing resources to perform real-time rendering for the rendering task according to the rendering resource request instruction, thereby obtaining a first rendered image.

S305: The cloud rendering platform sends the first rendered image to the second user equipment through the network device. Correspondingly, the second user equipment receives the first rendered image sent by the cloud rendering platform through the network device.

In a specific embodiment of the present application, the cloud rendering platform calculates first pricing information according to allocating computing resources to the rendering task, where the first pricing information is a fee for rendering the first rendered image.

S306: The first user equipment acquires the rendering resource adjustment instruction input by the user.

In a specific embodiment of the present application, the rendering resource adjustment instruction is to improve the rendering quality, or to reduce the rendering quality.

S307: The first user equipment sends the rendering resource adjustment instruction to the cloud rendering platform through the network device.

S308: The cloud rendering platform adjusts the computing resources allocated for the rendering task according to the rendering resource adjustment instruction, and uses the adjusted computing resources allocated for the rendering task to render the rendering task, Thereby, a second rendered image is obtained.

In a specific embodiment of the present application, in the case that the rendering resource adjustment instruction is used to improve the rendering quality of the rendering task, the cloud rendering platform adjusts the amount allocated for the rendering task according to the rendering resource adjustment instruction The computing resources include: increasing the computing resources allocated for the rendering task; or when the rendering resource adjustment instruction is used to reduce the rendering quality of the rendering task, the cloud rendering platform adjusts according to the rendering resource adjustment instruction The computing resources allocated for the rendering task include: reducing the computing resources allocated for the rendering task.

S309: The cloud rendering platform sends the second rendered image to the second user equipment through the network device.

In a specific embodiment of the present application, the cloud rendering platform calculates second pricing information according to the adjusted computing resources allocated to the rendering task, where the second pricing information is a fee for rendering the second rendered image.

For the sake of simplicity, the rendering quality adjustment method will not be introduced here. For details, please refer to FIGS. 1A-1B, 2-4, 5A-5B, 6, 7A-7B, 9A-9B, 10A-10B, 11A-11B and related descriptions.

Referring to FIG. 15 , FIG. 15 is a cloud rendering platform according to an embodiment proposed in this application, including: an acquisition module 610 , a receiving module 620 , and a rendering engine 630 .

The obtaining module 610 is used to obtain rendering tasks;

The receiving module 620 is configured to receive a rendering resource request instruction sent by the user equipment;

The rendering engine 630 is configured to allocate computing resources to the rendering task for rendering according to the rendering resource request instruction, thereby obtaining a first rendered image;

The receiving module 620 is configured to receive a rendering resource adjustment instruction sent by the user equipment;

The rendering engine 630 is configured to adjust the computing resources allocated for the rendering task according to the rendering resource adjustment instruction;

The rendering engine 630 is configured to render the rendering task using the adjusted computing resources allocated for the rendering task, thereby obtaining a second rendered image.

For the sake of simplicity, the cloud rendering platform is not described in detail here. For details, please refer to Fig. 1A-Fig. 1B, Fig. 2-Fig. 4, Fig. 5A-Fig. 5B, Fig. 6, Fig. 7A-Fig. 7B, Fig. 8A-Fig. 8B, FIGS. 9A-9B, 10A-10B, 11A-11B and related descriptions.

A cloud rendering system according to an embodiment of the present application includes user equipment, network equipment, and a cloud rendering platform. The user equipment may communicate with the cloud rendering platform through a network device. The user equipment may be a VR device, a computer, a smartphone, and the like. The cloud rendering platform includes one or more cloud rendering nodes.

Taking the user equipment as an intelligent terminal as an example, FIG. 16 is a structural block diagram of an intelligent terminal in an implementation manner. As shown in FIG. 16 , the smart terminal may include: a baseband chip 710 , a memory 715 including one or more computer-readable storage media, a radio frequency (RF) module 716 , and a peripheral system 717 . These components may communicate on one or more communication buses 714 .

The peripheral system 717 is mainly used to realize the interaction function between the intelligent terminal and the user/external environment, and mainly includes the input and output devices of the intelligent terminal. In a specific implementation, the peripheral system 717 may include: a touch screen controller 718 , a key controller 719 , an audio controller 720 and a sensor management module 721 . Among them, each controller can be coupled with its corresponding peripheral devices, such as the touch screen 723 , the keys 724 , the audio circuit 725 and the sensor 726 . In some embodiments, a gesture sensor in sensor 726 may be used to receive user input for gesture control operations. The pressure sensor in the sensor 726 may be disposed below the touch screen 723 , and may be used to collect the touch pressure acting on the touch screen 723 when the user inputs a touch operation through the touch screen 723 . It should be noted that the peripheral system 717 may also include other I/O peripherals.

The baseband chip 710 may integrally include: one or more processors 711 , a clock module 712 and a power management module 713 . The clock module 712 integrated in the baseband chip 710 is mainly used to generate the clock required for data transmission and timing control for the processor 711 . The power management module 713 integrated in the baseband chip 710 is mainly used to provide a stable and high-precision voltage for the processor 711 , the radio frequency module 716 and peripheral systems.

The radio frequency (RF) module 716 is used for receiving and transmitting radio frequency signals, and mainly integrates the receiver and the transmitter of the smart terminal. A radio frequency (RF) module 716 communicates with communication networks and other communication devices via radio frequency signals. In a specific implementation, the radio frequency (RF) module 716 may include, but is not limited to, an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chip, a SIM card, and storage media, etc. In addition, the radio frequency module 716 may also include short-range wireless communication modules such as WIFI, Bluetooth, and the like. In some embodiments, radio frequency (RF) module 716 may be implemented on a separate chip.

The memory 715 may include random access memory (Random Access Memory, RAM), flash memory (FlashMemory), etc., or may be RAM, read-only memory (Read-Only Memory, ROM) or hard disk (HardDisk Drive, HDD) or solid state Hard disk (Solid-StateDrive, SSD). The memory 815 may store operating systems, communication programs, user interface programs, browsers, rendering applications. The rendering applications include game applications and other rendering applications.

Taking the user equipment as a computer as an example, FIG. 17 is a structural block diagram of a computer in an implementation manner. As shown in FIG. 17 , the computer may include a host 810 , an output device 820 and an input device 830 .

The host 810 may integrally include: one or more processors, a clock module, and a power management module. The clock module integrated in the host 810 is mainly used to generate clocks required for data transmission and timing control for the processor. The power management module integrated in the host 810 is mainly used to provide stable and high-accuracy voltages for the processor, the output device 820 and the input device 830 . The host 810 also integrates memory for storing various software programs and/or sets of instructions. In particular implementations, the memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory can store operating systems, such as ANDROID, IOS, WINDOWS, or embedded operating systems such as LINUX. The memory can also store a communication program that can be used to communicate with one or more input devices or output devices. The memory can also store a user interface program, which can vividly display the contents of the browser through a graphical operation interface, and receive user control operations on the browser through input controls such as menus, dialog boxes, and keys. The memory may also store operating systems, communication programs, user interface programs, browsers, and rendering applications, among others. The rendering applications include game applications and other rendering applications.

The output device 820 mainly includes a display, and the display may include a cathode ray tube display (CRT), a plasma display (PDP), a liquid crystal display (LCD), and the like. Taking the display as an LCD as an example, the liquid crystal display includes a liquid crystal panel and a backlight module, wherein the liquid crystal display panel includes a polarizing film, a glass substrate, a black matrix, a color filter, a protective film, a common electrode, a calibration layer, and a liquid crystal layer (liquid crystal layer). , spacers, sealants), capacitors, display electrodes, prism layers, diffuser layers. The backlight module includes: lighting source, reflective plate, light guide plate, diffuser, brightness enhancement film (prism sheet) and frame, etc.

Input devices 830 may include a keyboard and a mouse. Keyboard and mouse are the most commonly used and most important input devices. Through the keyboard, English letters, numbers, punctuation marks, etc. can be input into the computer, so as to issue commands and input data to the computer. This simplifies the operation. Wherein, the keyboard may include mechanical keyboard, plastic film keyboard (Mechanical), conductive rubber keyboard (Membrane), contactless electrostatic capacitive keyboard (Capacitives), etc., and the mouse may include rolling mouse, optical mouse, wireless mouse and so on.

FIG. 18 is a structural block diagram of a cloud rendering platform in an implementation manner. The cloud rendering platform may include one or more cloud rendering nodes. The cloud rendering node includes: a processing system 910 , a first memory 920 , an intelligent network card 930 and a bus 940 .

The processor system 910 may adopt a heterogeneous structure, that is, include one or more general-purpose processors, and one or more special processors, such as GPUs or AI chips, etc., wherein the general-purpose processors may be capable of processing Any type of equipment for electronic instructions, including a central processing unit (Central Processing Unit, CPU), a microprocessor, a microcontroller, a main processor, a controller, an application specific integrated circuit (ASIC), and the like. The general-purpose processor executes various types of digitally stored instructions, such as software or firmware programs stored in the first memory 920 . In a specific embodiment, the general purpose processor may be an x86 processor or the like. The general-purpose processor sends commands to the first memory 920 through a physical interface to complete storage-related tasks. For example, the general-purpose processor may provide commands including read commands, write commands, copy commands, and erase commands. The commands may specify operations related to specific pages and blocks of the first memory 920 . Special processors are used to perform complex operations for image rendering and more.

The first memory 920 may include random access memory (Random Access Memory, RAM), flash memory (FlashMemory), etc., or may be RAM, read-only memory (Read-Only Memory, ROM) or hard disk (Hard Disk Drive, HDD) ) or solid-state drive (Solid-StateDrive, SSD). The first memory 920 stores program codes implementing the rendering engine and rendering applications.

The smart network card 930 is also called a network interface controller, a network interface card or a local area network (Local Area Network, LAN) adapter. Each smart network card 930 has a unique MAC address, which is burned into the read-only memory chip by the manufacturer of the smart network card 930 during production. The smart network card 930 includes a processor 931 , a second memory 932 and a transceiver 933 . The processor 931 is similar to a general purpose processor, however, the performance requirements of the processor 931 may be lower than those of a general purpose processor. In a specific embodiment, the processor 931 may be an ARM processor or the like. The second memory 932 may also be a flash memory, an HDD or an SDD, and the storage capacity of the second memory 932 may be smaller than that of the first memory 920 . The transceiver 933 can be used to receive and transmit messages, and upload the received messages to the processor 931 for processing. The smart network card 930 may also include a plurality of ports, and the ports may be any one or more of three types of interfaces, a thick cable interface, a thin cable interface, and a twisted pair interface.

For the sake of brevity, the cloud rendering system is not described in detail here. For details, please refer to Fig. 1A-Fig. 1B, Fig. 2-Fig. 4, Fig. 5A-Fig. 5B, Fig. 6, Fig. 7A-Fig. 7B, Fig. 8A-Fig. 8B, FIGS. 9A-9B, 10A-10B, 11A-11B and related descriptions. Moreover, the user equipment may perform the steps performed by the user equipment in the rendering quality adjustment method shown in FIG. 12 and FIG. 13 , and the cloud rendering platform may perform the steps performed by the cloud rendering platform in the rendering quality adjustment method shown in FIG. 12 and FIG. 13 . step. In addition, the acquiring module 610 and the receiving module 620 in FIG. 15 may be implemented by the smart network card 930 in this embodiment, and the rendering engine 630 in FIG. 15 may be executed by the processor system 910 in this embodiment The program in the first memory 920 code to implement.

In the above solution, when the user equipment sends the rendering resource adjustment instruction, the cloud rendering platform adjusts the computing resources in real time according to the rendering resource adjustment instruction for calculation, thereby dynamically adjusting the rendering quality and adapting to the different needs of the user.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server or data center Transmission to another website site, computer, server, or data center by wire (eg, coaxial cable, optical fiber, digital subscriber line) or wireless (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes an integration of one or more available media. The usable media may be magnetic media (eg, floppy disks, storage disks, magnetic tapes), optical media (eg, DVD), or semiconductor media (eg, Solid State Disk (SSD)), among others.

Claims (10)

1. A rendering quality adjustment method, comprising:

the cloud rendering platform acquires a rendering task;

the cloud rendering platform receives a rendering resource request instruction sent by user equipment;

the cloud rendering platform allocates computing resources for the rendering task according to the rendering resource request instruction to perform rendering, so that a first rendering image is obtained;

the cloud rendering platform receives a rendering resource adjusting instruction sent by the user equipment;

the cloud rendering platform adjusts the computing resources allocated to the rendering task according to the rendering resource adjusting instruction;

and the cloud rendering platform renders the rendering task by using the adjusted computing resources allocated to the rendering task, so as to obtain a second rendering image.

2. The method of claim 1,

when the rendering resource adjustment instruction is used to improve the rendering quality of the rendering task, the cloud rendering platform adjusting the computing resources allocated to the rendering task according to the rendering resource adjustment instruction includes: increasing the computational resources allocated for the rendering task; or

In a case that the rendering resource adjustment instruction is used to reduce the rendering quality of the rendering task, the cloud rendering platform adjusting the computing resources allocated to the rendering task according to the rendering resource adjustment instruction includes: reducing the computational resources allocated for the rendering task.

3. The method according to claim 1 or 2,

after the cloud rendering platform allocates computing resources for the rendering task according to the rendering resource request instruction to perform rendering, thereby obtaining a first rendering image, the method further includes:

calculating first pricing information according to computing resources allocated for the rendering task, wherein the first pricing information is a cost for rendering the first rendered image;

after the cloud rendering platform renders the rendering task by using the adjusted computing resources allocated to the rendering task, so as to obtain a second rendering image, the method further includes:

and calculating second pricing information according to the adjusted computing resources allocated for the rendering task, wherein the second pricing information is the cost for rendering the second rendering image.

4. The method of any one of claims 1 to 3, wherein the rendering task is rendered using ray tracing rendering, and the rendering resource request instruction includes one or more of a rendering index, a resource parameter, and a display parameter; the rendering index comprises one or more of sampling number Spp per pixel, light rebound times, object modeling triangular surface number, vertex number and picture noise, the resource parameter comprises one or more of the number of processors, dominant frequency of the processors, memory size and network bandwidth, and the display parameter comprises frame rate.

5. A cloud rendering platform, comprising: the system comprises an acquisition module, a receiving module and a rendering engine;

the acquisition module is used for acquiring a rendering task;

the receiving module is used for receiving a rendering resource request instruction sent by user equipment;

the rendering engine is used for allocating computing resources for the rendering task according to the rendering resource request instruction so as to perform rendering, and therefore a first rendering image is obtained;

the receiving module is used for receiving a rendering resource adjusting instruction sent by the user equipment;

the rendering engine is used for adjusting the computing resources distributed to the rendering task according to the rendering resource adjusting instruction;

and the rendering engine is used for rendering the rendering task by using the adjusted computing resources allocated to the rendering task so as to obtain a second rendering image.

6. The platform of claim 5,

when the rendering resource adjustment instruction is used to improve the rendering quality of the rendering task, the cloud rendering platform adjusting the computing resources allocated to the rendering task according to the rendering resource adjustment instruction includes: increasing the computational resources allocated for the rendering task; or

In a case that the rendering resource adjustment instruction is used to reduce the rendering quality of the rendering task, the cloud rendering platform adjusting the computing resources allocated to the rendering task according to the rendering resource adjustment instruction includes: reducing the computational resources allocated for the rendering task.

7. The platform of claim 5 or 6, wherein the cloud rendering platform further comprises a pricing module,

the pricing module is used for calculating first pricing information according to computing resources allocated for the rendering task, wherein the first pricing information is the cost for rendering the first rendering image;

the pricing module is further configured to calculate second pricing information according to the adjusted computing resources allocated to the rendering task, wherein the second pricing information is a cost for rendering the second rendered image.

8. The platform of any one of claims 5 to 7, wherein the rendering tasks are rendered using ray tracing rendering, and the rendering resource request instructions include one or more of rendering metrics, resource parameters, and display parameters; the rendering index comprises one or more of sampling number Spp per pixel, light rebound times, object modeling triangular surface number, vertex number and picture noise, the resource parameter comprises one or more of the number of processors, dominant frequency of the processors, memory size and network bandwidth, and the display parameter comprises frame rate.

9. A rendering platform comprising at least one rendering node, each rendering node comprising a processor and a memory, the processor executing a program in the memory to perform the method of any of claims 1 to 4.

10. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of any of claims 1 to 4.

Images