Cloud model rendering method, device and equipment and readable storage medium

Abstract

The invention discloses a cloud model rendering method, device and equipment and a readable storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a first intersection condition between the sight line of a camera and the top of a physical cloud layer, wherein the physical cloud layer comprises a virtual sub-layer; obtaining reference density information of candidate sampling points included in the cloud model and first position information relative to the virtual sub-layer; if the first intersection condition represents that a first intersection point exists between the sight line and the top of the physical cloud layer, acquiring a second intersection condition between the sight line and the bottom of the physical cloud layer; determining exposure information corresponding to the sight line based on the second intersection condition, the first position information of the candidate sampling points and the reference density information; and determining a rendering image of the cloud model based on the exposure information corresponding to the sight line. According to the method, the cloud model is controlled to be distributed in the virtual sub-layer, the exposure information corresponding to the sight line is determined according to the two-time intersection condition of the sight line of the camera and the physical cloud layer, the calculation amount is reduced, and the rendering efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and particularly to a rendering method, device, equipment and readable storage medium of a cloud model. Background Art

[0002] With the development of computer technology, industries such as games and film and television have higher and higher requirements for environmental rendering, and clouds are common elements in the environment. In some cases, a cloud model can be constructed first, and the cloud model is used to simulate clouds in nature. Then, rendering processes such as lighting calculation and projection transformation are performed on the cloud model to obtain a rendered image of the cloud model. Since clouds in nature have the characteristic of layered distribution, for example, according to the distribution height of cloud layers, cloud layers are divided into low-level clouds, middle-level clouds and high-level clouds. Therefore, how to render a cloud model with the characteristic of layered distribution has become a technical problem to be solved urgently. Summary of the Invention

[0003] The present application provides a rendering method, device, equipment and readable storage medium of a cloud model, which can realize the rendering of a cloud model with the characteristic of layered distribution and has relatively high rendering efficiency. The technical solutions include the following contents.

[0004] On the one hand, a rendering method of a cloud model is provided. The method includes:

[0005] Obtain a first intersection situation between a plurality of sight lines of a camera and the top of a physical cloud layer, where the physical cloud layer includes at least one virtual sub-layer, the physical cloud layer is used to distribute the cloud model, and the camera is used to perform virtual imaging on the cloud model;

[0006] Obtain the reference density information of a plurality of candidate sampling points included in the cloud model and the first position information of each candidate sampling point relative to the virtual sub-layer where it is located;

[0007] For any one of the sight lines of the camera, if the first intersection situation indicates that there is a first intersection point between the any one of the sight lines and the top of the physical cloud layer, then obtain a second intersection situation between the any one of the sight lines and the bottom of the physical cloud layer;

[0008] Based on the second intersection situation, the first position information and the reference density information of each candidate sampling point, determine the exposure information corresponding to the any one of the sight lines;

[0009] Based on the exposure information corresponding to each sight line, determine the rendered image of the cloud model.

[0010] On the other hand, a rendering device of a cloud model is provided. The device includes:

[0011] An acquisition module, configured to acquire a first intersection situation between a plurality of lines of sight of a camera and the top of a physical cloud layer, where the physical cloud layer includes at least one virtual sub-layer, the physical cloud layer is used to distribute a cloud model, and the camera is used to perform virtual imaging on the cloud model;

[0012] The acquisition module is further configured to acquire reference density information of a plurality of candidate sampling points included in the cloud model and first position information of each candidate sampling point relative to the virtual sub-layer where it is located;

[0013] The acquisition module is further configured to, for any one of the lines of sight of the camera, if the first intersection situation indicates that there is a first intersection point between the any one of the lines of sight and the top of the physical cloud layer, acquire a second intersection situation between the any one of the lines of sight and the bottom of the physical cloud layer;

[0014] A determination module, configured to determine exposure information corresponding to the any one of the lines of sight based on the second intersection situation, the first position information and the reference density information of each candidate sampling point;

[0015] The determination module is further configured to determine a rendered image of the cloud model based on the exposure information corresponding to each line of sight.

[0016] In a possible implementation manner, the first position information of the candidate sampling point includes the horizontal position of the candidate sampling point on a horizontal plane and the first height of the candidate sampling point relative to the virtual sub-layer where it is located;

[0017] The acquisition module is configured to acquire a horizontal control parameter and a vertical control parameter, where the horizontal control parameter is used to control the shape distribution of the cloud model on a horizontal plane, and the vertical control parameter is used to control the shape distribution of the cloud model on a vertical plane; based on the horizontal control parameter, determine the horizontal position and the first density information of the plurality of candidate sampling points; based on the vertical control parameter, determine the first height and the second density information of the plurality of candidate sampling points; based on the first density information and the second density information of the plurality of candidate sampling points, determine the reference density information of the plurality of candidate sampling points.

[0018] In a possible implementation manner, the acquisition module is configured to acquire noise data of a plurality of initial points; adjust the noise data of the plurality of initial points through the horizontal control parameter to obtain a density distribution mask, where the density distribution mask is used to reflect the horizontal position and the density information of each initial point; for the density information of any one of the initial points in the density distribution mask, if the density information of the any one of the initial points meets an information condition, determine the horizontal position of the any one of the initial points as the horizontal position of a candidate sampling point, and determine the density information of the any one of the initial points as the first density information of the one candidate point.

[0019] In a possible implementation, the obtaining module is configured to numerically adjust the horizontal control parameter to obtain an adjusted horizontal control parameter; and based on the adjusted horizontal control parameter, adjust the noise data of the multiple initial points to obtain a density distribution mask.

[0020] In a possible implementation, the vertical control parameter includes at least one of the following:

[0021] A bottom control parameter for controlling the bottom shape of the cloud model;

[0022] A top control parameter for controlling the top shape of the cloud model;

[0023] A transition position control parameter for controlling the transition from the bottom shape to the top shape of the cloud model;

[0024] A frequency parameter for controlling the appearance frequency of the edge details of the cloud model;

[0025] A size parameter for controlling the size of the edge details of the cloud model;

[0026] A transition mode control parameter for controlling the transition from the bottom shape to the top shape of the cloud model.

[0027] In a possible implementation, the obtaining module is configured to obtain the top radius of the physical cloud layer; for any line of sight of the camera, based on the top radius of the physical cloud layer and the ray function for characterizing the any line of sight, determine the first intersection situation.

[0028] In a possible implementation, the obtaining module is configured to obtain the radius of the planet, the thickness of the physical cloud layer, and the first distance between the bottom of the physical cloud layer and the surface of the planet; based on the radius of the planet and the first distance, determine the bottom radius of the physical cloud layer; based on the bottom radius of the physical cloud layer and the thickness of the physical cloud layer, determine the top radius of the physical cloud layer.

[0029] In a possible implementation, the obtaining module is configured to obtain the bottom radius of the physical cloud layer; based on the bottom radius of the physical cloud layer and the ray function for characterizing the any line of sight, determine the second intersection situation.

[0030] In a possible implementation, there is one first intersection point;

[0031] The determining module is configured to, if the second intersection situation indicates that there is no second intersection point between any of the sight lines and the bottom of the physical cloud layer, determine the exposure information corresponding to any of the sight lines based on the distance between the camera and the first intersection point, the first position information of each candidate sampling point, and the reference density information; if the second intersection situation indicates that there is one second intersection point, determine the exposure information corresponding to any of the sight lines based on the second distance, the first position information of each candidate sampling point, and the reference density information, where the second distance is the distance between the first intersection point and the second intersection point or the distance between the camera and the first intersection point; if the second intersection situation indicates that there are two second intersection points, determine the exposure information corresponding to any of the sight lines based on the distance between the first intersection point and the third intersection point that is closer to the first intersection point among the two second intersection points, the distance between the camera and the other second intersection point except the third intersection point among the two second intersection points, the first position information of each candidate sampling point, and the reference density information.

[0032] In a possible implementation, there are two first intersection points;

[0033] The determining module is configured to, if the second intersection situation indicates that there is no second intersection point or there is one second intersection point between any of the sight lines and the bottom of the physical cloud layer, determine the exposure information corresponding to any of the sight lines based on the distance between the two first intersection points, the first position information of each candidate sampling point, and the reference density information; if the second intersection situation indicates that there are two second intersection points, determine the exposure information corresponding to any of the sight lines based on the distance between one first intersection point and the fourth intersection point that is closer to the one first intersection point among the two second intersection points, the distance between the other first intersection point and the other second intersection point except the fourth intersection point among the two second intersection points, the first position information of each candidate sampling point, and the reference density information.

[0034] In a possible implementation, the determining module is configured to obtain the third density information of the multiple candidate sampling points and the second position information of each candidate sampling point relative to the physical cloud layer; determine the exposure information corresponding to any of the sight lines based on the second intersection situation, the first position information, the second position information, the third density information, and the reference density information of each candidate sampling point.

[0035] On the other hand, an electronic device is provided, where the electronic device includes a processor and a memory, and at least one computer program is stored in the memory. The at least one computer program is loaded and executed by the processor so that the electronic device implements the rendering method of the cloud model described in any one of the above.

[0036] On the other hand, a computer-readable storage medium is also provided. At least one computer program is stored in the computer-readable storage medium and is loaded and executed by a processor to enable an electronic device to implement the rendering method of the cloud model described in any one of the above.

[0037] On the other hand, a computer program is also provided. The computer program is at least one, and the at least one computer program is loaded and executed by a processor to enable an electronic device to implement any one of the above cloud model rendering methods.

[0038] On the other hand, a computer program product is also provided. At least one computer program is stored in the computer program product, and the at least one computer program is loaded and executed by a processor to enable an electronic device to implement any one of the above cloud model rendering methods.

[0039] The technical solution provided by this application at least brings the following beneficial effects:

[0040] In the technical solution provided by this application, the physical cloud layer includes at least one virtual sub-layer. By configuring the first position information of each candidate sampling point included in the cloud model relative to the virtual sub-layer where it is located, the distribution of the cloud model within the virtual sub-layer is controlled, that is, the hierarchical distribution of the cloud model is controlled. In addition, first obtain the first intersection situation between the line of sight of the camera and the top of the physical cloud layer. When the first intersection situation indicates that there is a first intersection point between the line of sight and the top of the physical cloud layer, obtain the second intersection situation between the line of sight of the camera and the bottom of the physical cloud layer, and determine the exposure information corresponding to the line of sight based on the second intersection situation. The exposure information corresponding to the line of sight is determined through the two intersection situations between the line of sight of the camera and the physical cloud layer, reducing the amount of calculation and improving the determination efficiency of the exposure information, so that the rendering image can be quickly determined based on the exposure information corresponding to the line of sight, improving the rendering efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 is a schematic diagram of the implementation environment of a cloud model rendering method provided by an embodiment of this application;

[0043] Figure 2 is a flowchart of a cloud model rendering method provided by an embodiment of this application;

[0044] Figure 3It is a schematic diagram of a physical cloud layer and a virtual sub-layer provided by an embodiment of the present application;

[0045] Figure 4 It is a schematic diagram of the parameters of a physical cloud layer provided by an embodiment of the present application;

[0046] Figure 5 It is a schematic diagram of the generation of a density distribution mask provided by an embodiment of the present application;

[0047] Figure 6 It is a schematic diagram of controlling the vertical modeling distribution through vertical control parameters provided by an embodiment of the present application;

[0048] Figure 7 It is a schematic diagram of edge details with different levels of fineness provided by an embodiment of the present application;

[0049] Figure 8 It is a schematic diagram of a cloud shape provided by an embodiment of the present application;

[0050] Figure 9 It is a schematic diagram of determining the exposure information corresponding to the line of sight provided by an embodiment of the present application;

[0051] Figure 10 It is a schematic diagram of a normalized height provided by an embodiment of the present application;

[0052] Figure 11 It is a schematic diagram of a cloud distribution provided by an embodiment of the present application;

[0053] Figure 12 It is a schematic diagram of the rendering of a cloud model provided by an embodiment of the present application;

[0054] Figure 13 It is a schematic diagram of a rendered image of the perspective between cloud layers provided by an embodiment of the present application;

[0055] Figure 14 It is a schematic diagram of a rendered image of the top-down perspective provided by an embodiment of the present application;

[0056] Figure 15 It is a schematic diagram of a rendered image of the long viewing distance between cloud layers provided by an embodiment of the present application;

[0057] Figure 16 It is a schematic diagram of a rendered image of the medium viewing distance between cloud layers provided by an embodiment of the present application;

[0058] Figure 17 It is a schematic diagram of a rendered image of the short viewing distance between cloud layers provided by an embodiment of the present application;

[0059] Figure 18 It is a schematic diagram of a rendered image of the upward perspective provided by an embodiment of the present application;

[0060] Figure 19 It is a schematic diagram of a rendered image with different moving speeds provided by an embodiment of the present application;

[0061] Figure 20 It is a schematic structural diagram of a rendering device for a cloud model provided by an embodiment of the present application;

[0062] Figure 21 It is a schematic structural diagram of a terminal device provided by an embodiment of the present application;

[0063] Figure 22 It is a schematic structural diagram of a server provided by an embodiment of the present application. Detailed implementation manners

[0064] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0065] With the development of computer technology, industries such as games and movies have higher and higher requirements for environmental rendering, and clouds are common elements in the environment. Generally, cloud models can be used to simulate clouds in nature, and the computer renders the cloud models to obtain rendered images of the cloud models. Since clouds in nature have the characteristic of layered distribution, therefore, how to render cloud models with the characteristic of layered distribution has become a technical problem that urgently needs to be solved.

[0066] For the above reasons, an embodiment of the present application provides a rendering method for a cloud model, which can render a cloud model with the characteristic of layered distribution and can improve the rendering efficiency.

[0067] Figure 1 It is a schematic diagram of the implementation environment of a rendering method for a cloud model provided by an embodiment of the present application. As Figure 1 shown, the implementation environment includes a terminal device 101 and a server 102. Among them, the rendering method for the cloud model in the embodiment of the present application can be executed by the terminal device 101, or can be executed by the server 102, or can be jointly executed by the terminal device 101 and the server 102.

[0068] The terminal device 101 can be a smart phone, a game console, a desktop computer, a tablet computer, a laptop computer, a smart TV, a smart vehicle-mounted device, a smart voice interaction device, a smart home appliance, etc. The server 102 can be a single server, or a server cluster composed of multiple servers, or any one of a cloud computing platform and a virtualization center, which is not limited in the embodiments of the present application. The server 102 can be connected to the terminal device 101 through a communication network, and the communication network can be a wired network or a wireless network. The server 102 can have functions such as data processing, data storage, and data transceiver, which are not limited in the embodiments of the present application. The number of the terminal device 101 and the server 102 is not limited, and can be one or more.

[0069] The optional embodiments of the present application can be implemented based on cloud technology. Cloud technology is a hosting technology that unifies a series of resources such as hardware, software, and network within a wide area network or a local area network to achieve data computing, storage, processing, and sharing.

[0070] Cloud technology is a general term for network technology, information technology, integration technology, management platform technology, application technology, etc. applied based on the cloud computing business model. It can form a resource pool, be used on demand, and be flexible and convenient. Cloud computing technology will become an important support. The background services of the technical network system require a large amount of computing and storage resources, such as video websites, picture websites, and more portal websites. With the high development and application of the Internet industry, in the future, each item may have its own identification mark and needs to be transmitted to the background system for logical processing. Data at different levels will be processed separately, and various industry data requires a powerful system background support, which can only be achieved through cloud computing.

[0071] The embodiments of the present application provide a rendering method for a cloud model. The method can be applied to the above implementation environment, can render a cloud model with a hierarchical distribution characteristic, and has a high rendering efficiency. Taking Figure 2 the flowchart of a rendering method for a cloud model provided by the embodiments of the present application shown as an example, for the convenience of description, the terminal device 101 or the server 102 that executes the rendering method for the cloud model in the embodiments of the present application is called an electronic device, and the method can be executed by the electronic device. As Figure 2 shown, the method includes the following steps.

[0072] Step 201, obtain a first intersection situation between a plurality of sight lines of a camera and the top of a physical cloud layer. The physical cloud layer includes at least one virtual sub-layer, the physical cloud layer is used to distribute the cloud model, and the camera is used to perform virtual imaging on the cloud model.

[0073] For the convenience of explanation and illustration, several terms involved in step 201 will be explained and illustrated below.

[0074] Camera: In the field of rendering of cloud models, a camera refers to a virtual camera. Generally, the rendering pipeline in an electronic device can render a rendered image of an object model through given scene elements such as a virtual camera, an object model, and a light source. The rendering process can be generally summarized as capturing the object model in the light source environment through the virtual camera to obtain the rendered image of the object model. That is to say, the virtual camera is used to perform virtual imaging on the object model. In the embodiments of the present application, the object model includes but is not limited to cloud models. For example, it can also be a building model, a mountain model, etc. Based on this, the virtual camera can be used to perform virtual imaging on cloud models and / or other models outside the cloud model to obtain a rendered image.

[0075] Generally, a camera corresponds to a frustum. When the object model is within the frustum, the camera can image the object model to obtain a rendered image. In the embodiments of the present application, taking the position of the camera as the starting point, the ray pointing from the starting point to any point within the frustum is called the line of sight of the camera. That is to say, the line of sight of the camera is the ray pointing from the camera to any point located within the frustum.

[0076] Physical cloud layer: The physical cloud layer is a spherical shell composed of an upper spherical surface and a lower spherical surface for distributing cloud models. Here, the upper spherical surface is the top of the physical cloud layer, and the lower spherical surface is the bottom of the physical cloud layer. The distance between the top of the physical cloud layer and the bottom of the physical cloud layer is the thickness of the physical cloud layer mentioned below. The physical cloud layer is used to distribute cloud models, but is not limited to cloud models. For example, the physical cloud layer can also distribute bird models, aircraft models, etc. The physical cloud layer includes at least one virtual sub-layer.

[0077] Virtual sub-layer: A virtual sub-layer is a sub-layer (Sub-Layer) with a specific thickness extracted from the physical cloud layer. Similar to the physical cloud layer, a virtual sub-layer is a spherical shell composed of an upper spherical surface and a lower spherical surface for distributing cloud models. Here, the upper spherical surface is the top of the virtual sub-layer, and the lower spherical surface is the bottom of the virtual sub-layer. The distance between the top of the virtual sub-layer and the bottom of the virtual sub-layer is the thickness of the virtual sub-layer mentioned below. It should be noted that the virtual sub-layer is located within the physical cloud layer. The bottom of the virtual sub-layer can be or not be the bottom of the physical cloud layer, and the top of the virtual sub-layer can be or not be the top of the physical cloud layer. The number of virtual sub-layers is one or more, and there may or may not be an interval between two adjacent virtual sub-layers. The cloud model can be located within the virtual sub-layer, but is not limited to within the virtual sub-layer. That is to say, the cloud model can be located within one virtual sub-layer, or within different virtual sub-layers, or simultaneously within the interval between the virtual sub-layer and the virtual sub-layer, or within the interval between the virtual sub-layers.

[0078] Cloud model: A model used to simulate clouds in nature. That is, the cloud model is used to reflect clouds in nature, and the types, shapes, textures, sizes, quantities, etc. of clouds can be characterized by the cloud model. That is to say, the cloud model can reflect one or more clouds, and the types, shapes, textures, etc. of any one cloud are not limited. For example, a cloud can be any one of stratus clouds, cumulus clouds, cirrus clouds, etc.

[0079] In the embodiments of the present application, the camera corresponds to multiple lines of sight. For any one line of sight, the electronic device can obtain the first intersection situation between the line of sight and the top of the physical cloud layer. The first intersection situation can characterize whether the line of sight intersects with the top of the physical cloud layer. In the embodiments of the present application, the intersection point when the line of sight intersects with the top of the physical cloud layer is called the first intersection point, and the first intersection situation can also characterize the number of first intersection points in the case where the line of sight intersects with the top of the physical cloud layer. The embodiments of the present application do not limit the manner of obtaining the first intersection situation, and one possible manner of obtaining is shown below.

[0080] In an exemplary embodiment, step 201 includes steps 2011 to 2012 (not shown in the figure).

[0081] Step 2011, obtain the top radius of the physical cloud layer.

[0082] As mentioned above, the top of the physical cloud layer is an upper spherical surface, and the size of the top of the physical cloud layer can be described by the top radius of the physical cloud layer. The embodiments of the present application do not limit the manner in which the electronic device obtains the top radius of the physical cloud layer. Exemplarily, the electronic device can obtain the top radius of the physical cloud layer input by the user, or the electronic device can calculate the top radius of the physical cloud layer.

[0083] In an exemplary embodiment, step 2011 includes: obtaining the radius of the planet, the thickness of the physical cloud layer, and the first distance between the bottom of the physical cloud layer and the surface of the planet; determining the bottom radius of the physical cloud layer based on the radius of the planet and the first distance; and determining the top radius of the physical cloud layer based on the bottom radius of the physical cloud layer and the thickness of the physical cloud layer.

[0084] Generally, the physical cloud layer is located above the surface of the planet, and there is a certain distance between the bottom of the physical cloud layer and the surface of the planet. The planet can be a sphere or approximately a sphere, and the size of the planet can be characterized by the radius of the planet. In addition, the distance between the top of the physical cloud layer and the bottom of the physical cloud layer is called the thickness of the physical cloud layer, and the distance between the bottom of the physical cloud layer and the surface of the planet is called the first distance. The electronic device can obtain the radius of the planet, the thickness of the physical cloud layer, and the first distance, and the manner of obtaining is not limited.

[0085] Exemplarily, the electronic device can obtain the radius of a planet, the thickness of a physical cloud layer, and a first distance that are input by the user, read from other devices, or calculated. Taking the thickness of the physical cloud layer as an example, the method for obtaining other parameters will not be elaborated herein.

[0086] In an exemplary embodiment, the physical cloud layer includes at least one virtual sub-layer. The distance between the bottom and the top of each virtual sub-layer is the thickness of the virtual sub-layer. The electronic device can obtain the thickness of each virtual sub-layer. For example, the user can input the thickness of each virtual sub-layer into the electronic device. In addition, there may be a gap between two adjacent virtual sub-layers, and the gap is the distance between the bottom of the previous virtual sub-layer and the top of the next virtual sub-layer. The thickness of the physical cloud layer can be determined based on the thickness of each virtual sub-layer and the gap between two adjacent virtual sub-layers.

[0087] It can be understood that the physical cloud layer includes one virtual sub-layer or at least two virtual sub-layers with a hierarchical relationship. Among them, the top of the first virtual sub-layer is the top of the physical cloud layer, or the top of the physical cloud layer is above the top of the first virtual sub-layer. The bottom of the previous virtual sub-layer is the top of the next virtual sub-layer or above the top of the next virtual sub-layer. The bottom of the last virtual sub-layer is the bottom of the physical cloud layer, or above the bottom of the physical cloud layer. Based on this, the distance between the top of the first virtual sub-layer and the top of the physical cloud layer, the thickness of each virtual sub-layer, the gap between every two adjacent virtual sub-layers, and the distance between the bottom of the last virtual sub-layer and the bottom of the physical cloud layer can be added together to obtain the thickness of the physical cloud layer.

[0088] Optionally, the top of the first virtual sub-layer is the top of the physical cloud layer, the bottom of the previous virtual sub-layer is above the top of the next virtual sub-layer, and the bottom of the last virtual sub-layer is the bottom of the physical cloud layer. In this case, the thickness of the physical cloud layer is: PhysicalLayerHeight = BH × N + Gap × (N - 1). Wherein, PhysicalLayerHeight represents the thickness of the physical cloud layer. BH (full name: BaseCloudHeight) represents the thickness of the virtual sub-layer. N represents the number of virtual sub-layers. Gap (full name: DistanceBetweenLayer) represents the gap between two adjacent virtual sub-layers.

[0089] As Figure 3As shown, the physical cloud layer includes virtual sub-layer 1 and virtual sub-layer 2. The top of the physical cloud layer is the top of virtual sub-layer 2, and the bottom of the physical cloud layer is the bottom of virtual sub-layer 1. There is a certain interval between the top of virtual sub-layer 1 and the bottom of virtual sub-layer 2. Among them, label 701 represents the interval between virtual sub-layer 1 and virtual sub-layer 2, and label 702 represents the thickness of virtual sub-layer 1 and also represents the thickness of virtual sub-layer 2. Based on the value of label 701 and the value of label 702, the value of label 705 can be determined. Label 705 represents the thickness of the physical cloud layer, which is the distance between the top of the physical cloud layer and the bottom of the physical cloud layer.

[0090] In the embodiments of the present application, the sum of the radius of the planet and the first distance can be used as the bottom radius of the physical cloud layer, and the sum of the bottom radius of the physical cloud layer and the thickness of the physical cloud layer can be used as the top radius of the physical cloud layer. Among them, the bottom radius of the physical cloud layer can be used to describe the size of the bottom of the physical cloud layer.

[0091] Please refer to Figure 4 , Figure 4 which is a schematic diagram of the parameters of a physical cloud layer provided by the embodiments of the present application. As Figure 4 shown, the three concentric circles from the inside to the outside sequentially represent the surface of the planet, the bottom of the physical cloud layer, and the top of the physical cloud layer. The physical cloud layer is between the bottom of the physical cloud layer and the top of the physical cloud layer, and cloud models are distributed within the physical cloud layer. Virtual imaging of the cloud models can be performed through a camera. Among them, the camera can be located above the physical cloud layer (such as in space), can also be located within the physical cloud layer, or can be located below the physical cloud layer (such as the surface of the planet). The line of sight of the camera can pass through or not pass through the cloud models.

[0092] In Figure 4 , taking the center of the planet as the coordinate system origin 0, the coordinate system origin 0 can be represented by the parameter CloudLayerCenterKm. The distance between the coordinate system origin 0 and the surface of the planet is the radius 1 of the planet, and the radius 1 of the planet can be represented by the parameter PlanetRadiusKm. The distance between the bottom of the physical cloud layer and the surface of the planet is the first distance 2, and the first distance 2 can be represented by the parameter LayerBottomAltitudeKm. The distance between the top of the physical cloud layer and the bottom of the physical cloud layer is the thickness 4 of the physical cloud layer, and the thickness 4 of the physical cloud layer can be represented by the parameter LayerHeightKm. Adding the radius 1 of the planet and the first distance 2, the bottom radius 3 of the physical cloud layer is obtained, and the bottom radius 3 of the physical cloud layer can be represented by the parameter BottomRadiusKm. Adding the bottom radius 3 of the physical cloud layer and the thickness 4 of the physical cloud layer, the top radius 5 of the physical cloud layer is obtained, and the top radius 5 of the physical cloud layer can be represented by the parameter TopRadiusKm.

[0093] Optionally, the origin 0 of the coordinate system, the radius 1 of the planet, the bottom radius 3 of the physical cloud layer, and the top radius 5 of the physical cloud layer are four core parameters. Through these four core parameters, the height AltitudeInLayer of the candidate sampling point in the physical cloud layer, the normalized height NormAltitudeInLayer of the candidate sampling point relative to the physical cloud layer, the height of the candidate sampling point in the virtual sub-layer, and the normalized height NormAltitudeInVSL of the candidate sampling point relative to the virtual sub-layer can be defined. The definition methods are described correspondingly below and will not be elaborated here for the time being.

[0094] Step 2012, for any line of sight of the camera, based on the top radius of the physical cloud layer and the ray function for characterizing any line of sight, determine the first intersection situation.

[0095] In the embodiments of the present application, a circle function, an arc function, etc. corresponding to the top of the physical cloud layer can be determined based on the top radius of the physical cloud layer. For the convenience of description, the circle function, the arc function, etc. are referred to as the top function. In addition, the electronic device can determine the ray function for characterizing the line of sight. By solving the top function and the ray function, the first intersection situation can be obtained, and the solving process will not be elaborated here.

[0096] Step 202, obtain the reference density information of multiple candidate sampling points included in the cloud model and the first position information of each candidate sampling point relative to the virtual sub-layer where it is located.

[0097] In the embodiments of the present application, the cloud model includes multiple candidate sampling points, and each candidate sampling point corresponds to reference density information and first position information. The reference density information of the candidate sampling point is used to characterize the aggregation degree of the candidate sampling point. The greater the reference density information, the higher the aggregation degree of the candidate sampling point. Optionally, the density of the central region of the cloud in nature is large, while the density of the edge region is small. Based on this, the reference density information of the candidate sampling point located in the central region of the cloud model is greater than that of the candidate sampling point located in the edge region of the cloud model. The first position information of the candidate sampling point is used to characterize the position of the candidate sampling point relative to the virtual sub-layer where the candidate sampling point is located, that is, the position of the candidate sampling point in the virtual sub-layer where it is located.

[0098] The electronic device can obtain the reference density information and the first position information of each candidate sampling point. The embodiments of the present application do not limit the acquisition method. Exemplarily, the electronic device can obtain the reference density information and the first position information of each candidate sampling point input by the user. Alternatively, the electronic device can calculate and obtain the reference density information and the first position information of each candidate sampling point in the manner shown in steps 2021 to 2024 below.

[0099] In a possible implementation, the first position information of the candidate sampling point includes the horizontal position of the candidate sampling point on the horizontal plane and the first height of the candidate sampling point relative to the virtual sublayer where it is located.

[0100] In the embodiments of the present application, the first position information of the candidate sampling point includes the x coordinate, y coordinate, and z coordinate of the candidate sampling point. The horizontal position of the candidate sampling point on the horizontal plane is characterized by the x coordinate and y coordinate, and the first height of the candidate sampling point relative to the virtual sublayer where the candidate sampling point is located is characterized by the z coordinate. The first height may be the height of the candidate sampling point within the virtual sublayer where it is located, or the normalized height of the candidate sampling point within the virtual sublayer where it is located.

[0101] Exemplarily, the height of the candidate sampling point within the virtual sublayer where it is located refers to the distance between the candidate sampling point and the bottom of the virtual sublayer where it is located. The distance between the bottom and the top of the virtual sublayer is the total height of the virtual sublayer, that is, the thickness of the virtual sublayer. The normalized height of the candidate sampling point relative to the virtual sublayer where it is located refers to the ratio of the height of the candidate sampling point within the virtual sublayer where it is located to the total height of the virtual sublayer.

[0102] Optionally, if the candidate sampling point is located in the Nth virtual sublayer sorted in ascending order, the normalized height NormAltitudeInVSL of the candidate sampling point in the virtual sublayer can be expressed as: where AltitudeInLayer represents the height of the candidate sampling point within the physical cloud layer. BH represents the thickness of the virtual sublayer. N represents the number of virtual sublayers. Gap represents the interval between two adjacent virtual sublayers.

[0103] It should be noted that the height of the candidate sampling point within the physical cloud layer refers to the distance between the candidate sampling point and the bottom of the physical cloud layer. In addition, the distance between the bottom and the top of the physical cloud layer is the total height of the physical cloud layer, that is, the thickness of the physical cloud layer. The normalized height of the candidate sampling point relative to the physical cloud layer refers to the ratio of the height of the candidate sampling point within the physical cloud layer to the total height of the physical cloud layer.

[0104] Such as Figure 3As shown, reference numeral 703 is the distance between the candidate sampling point and the bottom of the physical cloud layer, that is, the height of the candidate sampling point within the physical cloud layer. Reference numeral 705 is the thickness of the physical cloud layer, and the ratio between the value of reference numeral 703 and the value of reference numeral 705 is the normalized height of the candidate sampling point within the physical cloud layer. The difference obtained by subtracting the values of reference numeral 701 and reference numeral 702 from the value of reference numeral 703 is the height of the candidate sampling point within virtual sub-layer 2. Reference numeral 702 is the thickness of virtual sub-layer 1 and also the thickness of virtual sub-layer 2. Dividing the height of the candidate sampling point within virtual sub-layer 2 by the value of reference numeral 702 gives the normalized height of the candidate sampling point within virtual sub-layer 2. In addition, reference numeral 704 is the distance between the bottom of the physical cloud layer and the surface of the planet.

[0105] Exemplarily, when the z coordinate of the candidate sampling point represents the normalized height of the candidate sampling point within the virtual sub-layer where it is located, the first position information of the candidate sampling point is also referred to as the normalized position information of the candidate sampling point within the virtual sub-layer.

[0106] In the embodiment of the present application, step 202 includes steps 2021 to 2024 (not shown in the figure).

[0107] Step 2021, obtain a horizontal control parameter and a vertical control parameter.

[0108] The embodiment of the present application does not limit the manner of obtaining the horizontal control parameter and the vertical control parameter. For example, the electronic device can obtain the horizontal control parameter and the vertical control parameter input by the user, or the electronic device stores the correspondence between the cloud type and the horizontal control parameter and the vertical control parameter. The electronic device determines the type of cloud reflected by the cloud model and obtains the corresponding horizontal control parameter and vertical control parameter based on this correspondence.

[0109] Among them, the horizontal control parameter is used to control the modeling distribution of the cloud model on the horizontal plane. Optionally, the modeling distribution of the cloud model on the horizontal plane can be referred to as the horizontal modeling distribution of the cloud model, and this horizontal modeling distribution is equivalent to the projection of the cloud model on the horizontal plane. At least one of the size, density, texture, shape, etc. of the horizontal modeling distribution can be controlled by the horizontal control parameter.

[0110] The vertical control parameter is used to control the modeling distribution of the cloud model on the vertical plane. Optionally, the modeling distribution of the cloud model on the vertical plane can be referred to as the vertical modeling distribution of the cloud model, and this vertical modeling distribution is equivalent to the projection of the cloud model on the vertical plane. At least one of the size, density, texture, shape, etc. of the vertical modeling distribution can be controlled by the vertical control parameter.

[0111] Step 2022, based on the horizontal control parameter, determine the horizontal positions and the first density information of a plurality of candidate sampling points.

[0112] In the embodiments of the present application, the electronic device may determine the horizontal positions of each candidate sampling point based on the horizontal control parameter. Among them, the horizontal position of the candidate sampling point on the horizontal plane is described by the x coordinate and y coordinate of the candidate sampling point mentioned above. In addition, the electronic device may also determine the first density information of each candidate sampling point through the horizontal control parameter. The first density information of the candidate sampling point is used to reflect the aggregation degree of the candidate sampling point. Optionally, the greater the first density information, the higher the aggregation degree of the candidate sampling point.

[0113] Optionally, step 2022 includes steps A1 to A3 (not shown in the figure).

[0114] Step A1, obtain the noise data of multiple initial points.

[0115] In the embodiments of the present application, the noise data of any initial point may be generated based on a noise generation algorithm. The noise generation algorithm is at least one kind, and the embodiments of the present application do not limit any kind of noise generation algorithm. Exemplarily, the noise generation algorithm may be at least one of a Perlin Noise generation algorithm, a Worley Noise generation algorithm, a Perlin-Worley Noise generation algorithm, etc.

[0116] Among them, the Perlin Noise generation algorithm is a natural noise generation algorithm, which has continuity in the function and can give consistent values when called multiple times. The Worley Noise generation algorithm is a cellular noise generation algorithm, and the noise value spreads outward from random feature points, making the Worley Noise look like there are individual cells. The Perlin-Worley Noise combines the characteristics of the Perlin Noise and the Worley Noise, having good continuity and cell effect.

[0117] Optionally, the electronic device may generate the noise data of any initial point corresponding to the noise generation algorithm based on any noise generation algorithm. In this way, the noise data of the initial points corresponding to each noise generation algorithm are obtained, that is, the respective noise data of the initial points are obtained.

[0118] Step A2, adjust the noise data of the multiple initial points through the horizontal control parameter to obtain a density distribution mask, and the density distribution mask is used to reflect the horizontal positions and density information of each initial point.

[0119] In the embodiments of the present application, if there are at least two noise generation algorithms, any initial point corresponds to at least two noise data. The respective noise data corresponding to an initial point can be fused. For example, at least one of addition, multiplication, weighted calculation, etc. is performed on the respective noise data corresponding to an initial point to obtain the fused noise data of the initial point. Then, the fused noise data of each initial point is adjusted by a horizontal control parameter to obtain a density distribution mask. If there is one noise generation algorithm, the noise data of each initial point can be directly adjusted by the horizontal control parameter to obtain a density distribution mask.

[0120] Exemplarily, the density distribution mask arranges each initial point according to the horizontal position of each initial point and records the corresponding density information, and can be used to reflect the horizontal position and density information of each initial point. For example, the density distribution mask is a layout carrying the horizontal positions and density information of 4×4 initial points.

[0121] Optionally, the initial point corresponds to an x coordinate and a y coordinate, and the horizontal position of the initial point is characterized by the x coordinate and the y coordinate. The density information of the initial point is used to reflect the aggregation degree of the initial point, and the greater the density information, the more aggregated the initial point.

[0122] In an exemplary embodiment, step A2 includes: numerically adjusting the horizontal control parameter to obtain an adjusted horizontal control parameter; based on the adjusted horizontal control parameter, adjusting the noise data of multiple initial points to obtain a density distribution mask.

[0123] In the embodiments of the present application, at least one adjustment such as numerical remapping, numerical scaling, numerical clamping, numerical mapping, etc. can be performed on the horizontal control parameter first to obtain an adjusted horizontal control parameter.

[0124] Exemplarily, the horizontal control parameter can be numerically remapped first based on the remapping function Remap() to obtain a remapped horizontal control parameter.

[0125] Optionally, the remapping function involves five parameters. The first parameter is the parameter to be numerically remapped, that is, the horizontal control parameter. The second parameter is the minimum value of the first numerical range where the parameter is located before numerical remapping, that is, the minimum value of the first numerical range where the horizontal control parameter is located. The third parameter is the maximum value of the first numerical range where the parameter is located before numerical remapping, that is, the maximum value of the first numerical range where the horizontal control parameter is located. The fourth parameter is the minimum value of the second numerical range where the parameter is located after numerical remapping, that is, the minimum value of the second numerical range where the remapped horizontal control parameter is located. The fifth parameter is the maximum value of the second numerical range where the parameter is located after numerical remapping, that is, the maximum value of the second numerical range where the remapped horizontal control parameter is located.

[0126] For example, the remapped horizontal control parameter is Remap(Coverage, 0.0, 0.2, 0.2, 0.0). Here, Remap represents the function symbol of the remapping function, Coverage represents the horizontal control parameter, and 0.0, 0.2, 0.2, 0.0 represent the minimum and maximum values of the first numerical range, and the minimum and maximum values of the second numerical range, respectively.

[0127] Next, the difference between the horizontal control parameter and the remapped horizontal control parameter is numerically scaled by a scaling factor to obtain a scaled parameter. For example, the scaled parameter is: CloudCoverage = (Coverage - Remap(Coverage, 0.0, 0.2, 0.2, 0.0)) × Scale. Here, Scale represents the scaling factor, which can be a value input by the user or a value set based on manual experience.

[0128] Then, the scaled parameter is numerically clamped by the clamping function Clamp() to obtain a clamped parameter.

[0129] Optionally, the clamping function involves three parameters. The first parameter is the parameter to be numerically clamped, that is, the horizontal control parameter. The second parameter is the minimum value of the third numerical range where the parameter is located after numerical clamping, that is, the minimum value of the third numerical range where the clamped parameter is located. The third parameter is the maximum value of the third numerical range where the parameter is located after numerical clamping, that is, the maximum value of the third numerical range where the clamped parameter is located. For example, the clamped parameter is: Clamp(CloudCoverage, -0.2, 3.0). Here, CloudCoverage represents the scaled parameter, and Clamp represents the function symbol of the clamping function.

[0130] After that, the clamped parameter is numerically mapped by a mapping function to obtain a mapping result. The mapping result is used as the adjusted horizontal control parameter, or the adjusted horizontal control parameter is calculated based on the mapping result. The embodiments of the present application do not limit the mapping function. Exemplarily, the mapping function includes at least one of a linear mapping function, a non-linear mapping function, an exponential function, a logarithmic function, a trigonometric function, etc.

[0131] Exemplarily, first perform a first linear mapping on the clamped parameters to obtain the first linear mapping result CloudCoverage = 1.0 - Clamp(CloudCoverage, -0.2, 3.0) / 6.5. Then, perform an exponential mapping on the first linear mapping result through the exponential function pow() to obtain the exponential mapping result. Optionally, the exponential function involves two parameters. The first parameter is the parameter to be exponentially mapped, that is, the first linear mapping result. The second parameter is the exponent. For example, the exponential mapping result is pow(CloudCoverage, 3.0). After that, perform a second linear mapping on the exponential mapping result to obtain the second linear mapping result CloudCoverage = pow(CloudCoverage, 3.0) - 0.25. Exemplarily, the second linear mapping result is the adjusted horizontal control parameter.

[0132] Next, based on the adjusted horizontal control parameter, adjust the noise data of multiple initial points to obtain a density distribution mask. Alternatively, first determine the fused noise data of each initial point based on the noise data of each initial point corresponding to at least two noise generation algorithms, and then adjust the fused noise data of multiple initial points based on the adjusted horizontal control parameter to obtain a density distribution mask. The embodiments of the present application do not limit the adjustment method.

[0133] Exemplarily, use the color adjustment function saturate() to adjust the noise data (or fused noise data) of multiple initial points based on the adjusted horizontal control parameter to obtain a density distribution mask. Optionally, the density distribution mask ConservativeDensity is expressed as: ConservativeDensity = saturate(layout.r × layout.g - CloudCoverage). Where layout.r represents the noise data of each initial point corresponding to a noise generation algorithm (for example, Perlin-Worley Noise), layout.g represents the noise data of each initial point corresponding to another noise generation algorithm (for example, Perlin Noise), and CloudCoverage represents the adjusted horizontal control parameter. Through the color adjustment function, the color of the image can be effectively changed, the brightness and darkness of the image can be enhanced or weakened, and the details of the image can be made more delicate and more layered.

[0134] Please refer to Figure 5 , Figure 5 which is a schematic diagram of the generation of a density distribution mask provided by the embodiments of the present application. As Figure 5As shown, the procedural distribution of Berlin-Worley noise and Berlin noise can be fitted to obtain a density distribution mask, and the fitting process includes the following four steps.

[0135] In the first step, the difference between the horizontal control parameter and the remapped horizontal control parameter is numerically scaled by a scaling factor to obtain a scaled parameter.

[0136] In the second step, the scaled parameter is numerically clamped and linearly mapped for the first time to obtain the first linear mapping result.

[0137] In the third step, the first linear mapping result is exponentially mapped and linearly mapped for the second time to obtain an adjusted horizontal control parameter.

[0138] In the fourth step, the Berlin-Worley noise and Berlin noise are adjusted based on the adjusted horizontal control parameter using a color adjustment function to obtain a density distribution mask.

[0139] It should be noted that the implementation methods of the above four steps have been introduced above and will not be elaborated here. By using the Berlin-Worley noise and Berlin noise as the basic signals of the distribution and performing numerical remapping based on the idea of numerical remapping, the density distribution mask is controlled by relying on the horizontal control parameter Coverage, thereby controlling the horizontal shape distribution of the cloud model.

[0140] Optionally, the horizontal control parameter Coverage is a floating-point number. By using a floating-point number, that is, a small number of parameters, the horizontal shape distribution of the cloud model is controlled, reducing the construction difficulty of the cloud model and improving the construction efficiency of the cloud model.

[0141] Step A3: For the density information of any initial point in the density distribution mask, if the density information of any initial point satisfies the information condition, the horizontal position of any initial point is determined as the horizontal position of a candidate sampling point, and the density information of any initial point is determined as the first density information of a candidate point.

[0142] In the embodiment of the present application, the density distribution mask includes the density information of multiple initial points, and the density information of any two initial points may be the same or different. For any initial point, if the density information of the initial point is greater than the density threshold, or the density information of the initial point is within a set range, etc., it is determined that the density information of the initial point satisfies the information condition. In this case, the horizontal position of the initial point is determined as the horizontal position of a candidate sampling point, and the density information of the initial point is determined as the first density information of a candidate point.

[0143] The embodiment of the present application does not limit the density threshold, set range, etc. For example, the density threshold is 0 or 0.05, etc., and the set range is 0 to 1 or 0.05 to 1, etc.

[0144] In step 2023, based on the vertical control parameters, determine the first height and the second density information of multiple candidate sampling points.

[0145] In the embodiments of the present application, the electronic device may determine the first height of multiple candidate sampling points based on the vertical control parameters. As described above, the first height of the candidate sampling point relative to the virtual sublayer where it is located is described by the z coordinate of the candidate sampling point. This first height is the height or normalized height of the candidate sampling point within its virtual sublayer. By determining the first height of each candidate sampling point relative to the virtual sublayer where it is located, the distribution of the cloud model within the virtual sublayer is controlled, thereby realizing the hierarchical distribution of the cloud model, making the distribution of the cloud model approximate the cloud distribution in nature, and improving the fidelity.

[0146] In addition, the electronic device may also determine the second density information of multiple candidate sampling points based on the vertical control parameters. The second density information is used to reflect the aggregation degree of the candidate sampling points. The greater the second density information, the higher the aggregation degree.

[0147] In an exemplary embodiment, the vertical control parameters include at least one of parameter 1 to parameter 6.

[0148] Parameter 1: The bottom control parameter for controlling the bottom shape of the cloud model. Optionally, the bottom control parameter may also be referred to as the Bottom Erosion Amount. Generally, the bottom of a cloud in nature is relatively flat. Therefore, in the embodiments of the present application, the Bottom Erosion Amount is used to control whether the bottom shape of the cloud model is "flat".

[0149] Parameter 2: The top control parameter for controlling the top shape of the cloud model. Optionally, the top control parameter may also be referred to as the Top Erosion Amount. Generally, the top of a cloud in nature fluctuates greatly, presenting a "rough" shape, similar to a tower shape. Therefore, in the embodiments of the present application, the Top Erosion Amount is used to control whether the top shape of the cloud model is tower-shaped.

[0150] Parameter 3: The transition position control parameter for controlling the transition from the bottom shape of the cloud model to the top shape of the cloud model. Optionally, the transition position control parameter is also called Taper, which is used to control the transition position from the bottom shape of the cloud model to the top shape of the cloud model, that is, to control from what position the cloud model starts to transition from the bottom shape to the top shape.

[0151] Parameter 4: Frequency parameter used to control the frequency of the edge details of the cloud model. Optionally, the frequency parameter is also called the Sub Noise Amount. Generally, the edges of natural clouds are in the shape of small bumps, which are called edge details. The frequency of the edge details is controlled by the Sub Noise Amount.

[0152] Parameter 5: A size parameter used to control the size of the edge details of the cloud model. Optionally, the size parameter is also called sub noise scale. Generally, there are multiple edge details, and the size of each edge detail can be controlled by sub noise scale.

[0153] Parameter 6: a transition control parameter for controlling the transition from the bottom shape of the cloud model to the top shape of the cloud model. Optionally, the transition control parameter is also called a variation along altitude parameter, which is used to describe the transition from the bottom shape of the cloud model to the top shape of the cloud model. The transition refers to the change of the cloud model in the altitude direction, including but not limited to the change length, transition speed, etc.

[0154] See also Figure 6 , Figure 6 Schematic diagram of controlling vertical shape distribution by vertical control parameters provided in an embodiment of the present application. Figure 6 As shown in the figure, the shape distribution of the cloud model on the vertical plane is controlled by six parameters: bottom erosion, top erosion, taper, detail noise amount, detail noise scaling, and change along height. The vertical shape distribution of the cloud model is controlled by a small number of parameters, which reduces the difficulty of constructing the cloud model and improves the efficiency of constructing the cloud model.

[0155] It should be noted that the detail noise amount and detail noise scaling are used to determine the fineness of edge details. Based on the detail noise amount and detail noise scaling, several edge details with different fineness can be designed. Figure 7 As shown, Figure 7 Schematic diagram of edge details of different degrees of fineness provided by the embodiment of the present application. Figure 7 The low frequency in FIG. 1 is a schematic diagram of edge details without using any additional three-dimensional (3D) noise for detail erosion, and the edge details are less refined. Figure 7 The intermediate frequency in the image is constructed by sampling a 64^3 volume texture to construct a fractal noise. Based on the edge details corresponding to the "low frequency", a schematic diagram of the edge details is carved out based on the fractal noise, which improves the fineness of the edge details. Figure 7The high frequency in it is a schematic diagram of the edge details carved on the basis of the edge details corresponding to the "intermediate frequency" using additional high-frequency noise when the device performance permits, further improving the fineness of the edge details.

[0156] It can be understood that the above shows that the horizontal and vertical modeling distributions of the cloud model are controlled by 7 core parameters, so as to realize the control of the modeling distribution of the cloud model. In actual application, fewer parameters or additional parameters can also be used to control the modeling distribution of the cloud model.

[0157] For example, the wind field of any virtual sublayer can be controlled by the wind field parameters of the virtual sublayer, and the cloud coverage rate of the virtual sublayer can be automatically scaled through the wind field. It can be understood that different virtual sublayers can correspond to the same or different wind field parameters.

[0158] The cloud models between at least two virtual sublayers can be linked through linkage parameters, so that the cloud models between different virtual sublayers have relevance in terms of movement speed, modeling distribution, etc. For example, the cloud model of virtual sublayer 1 is controlled to move in one direction through the linkage parameter, while the cloud model of virtual sublayer 2 moves in this direction or the opposite direction, forming a linkage effect. Of course, if there is no linkage parameter, that is to say, the cloud models between virtual sublayers have no relevance. In this case, the control of the modeling distribution, movement speed, etc. of the cloud models for different virtual sublayers can be realized separately.

[0159] At least one virtual sublayer or physical cloud layer, etc., can also be associated with the Time Of Day (TOD) module, and based on the data-driven control method, the modeling distribution, movement speed, etc. of the cloud model are controlled to perform day and night cycles. Even at least one virtual sublayer or physical cloud layer, etc., can be associated with the season module, and based on the data-driven control method, the modeling distribution, movement speed, etc. of the cloud model are controlled to perform seasonal changes.

[0160] Generally speaking, the embodiments of the present application can control the modeling distribution of the cloud model through a small number of parameters, enrich the modeling of the cloud model, reduce the construction difficulty of the cloud model, and improve the construction efficiency of the cloud model. In addition, the constructed cloud model conforms to the cloud modeling in nature.

[0161] As Figure 8 shown, Figure 8 is a schematic diagram of a cloud modeling provided by the embodiments of the present application. From Figure 8As can be seen, when looking down at the cloud sea in nature, the top of the cloud presents a "billowy" shape, while the bottom and the surroundings present a "wispy" shape. In the embodiment of the present application, the bottom control parameter is used to control the bottom of the cloud model to present a "flat" shape, and the top control parameter is used to control the top of the cloud model to present a "billowy" shape. By combining the control of the fineness of the edge details through the frequency parameter and the size parameter, and controlling the transition from the bottom shape to the top shape through the transition position control parameter and the transition method control parameter, the constructed cloud model is closer to the cloud in nature, improving the verisimilitude of the cloud model.

[0162] Step 2024: Determine the reference density information of the multiple candidate sampling points based on the first density information and the second density information of the multiple candidate sampling points.

[0163] In the embodiment of the present application, for any candidate sampling point, the first density information and the second density information of the candidate sampling point can be used as the reference density information of the candidate sampling point. Alternatively, the first density information and the second density information of the candidate sampling point are summed, averaged, weighted calculated, etc. to obtain the reference density information of the candidate sampling point.

[0164] Step 203: For any line of sight of the camera, if the first intersection situation indicates that there is a first intersection point between any line of sight and the top of the physical cloud layer, obtain the second intersection situation between any line of sight and the bottom of the physical cloud layer.

[0165] As mentioned above, the first intersection situation can indicate whether the line of sight intersects with the top of the physical cloud layer. If the line of sight does not intersect with the top of the physical cloud layer, it means that there is no first intersection point between the line of sight and the top of the physical cloud layer. In this case, the line of sight does not pass through the physical cloud layer. For example, the camera is located above the physical cloud layer and is aligned with the direction of space. Optionally, since the line of sight does not pass through the physical cloud layer, the exposure information corresponding to the line of sight can be set to a set value (such as 0), or in other ways, the exposure information corresponding to the line of sight is determined, and the determination method will not be elaborated here.

[0166] If the first intersection situation indicates that the line of sight intersects with the top of the physical cloud layer, it means that there is at least one first intersection point between the line of sight and the top of the physical cloud layer. In this case, the line of sight may or may not pass through the physical cloud layer, and there are various possibilities for the position between the camera and the physical cloud layer. For example, the camera is located above the physical cloud layer and is aligned with the direction of the physical cloud layer, or the camera is located between the physical cloud layers, or the camera is located below the physical cloud layer and is aligned with the direction of the physical cloud layer.

[0167] Due to the various possibilities of the relative position between the camera and the physical cloud layer, the electronic device can obtain a second intersection situation between the line of sight and the bottom of the physical cloud layer, and the second intersection situation can characterize whether there is an intersection between the line of sight and the bottom of the physical cloud layer. The intersection point when the line of sight intersects with the bottom of the physical cloud layer is called the second intersection point, and the second intersection situation can also characterize the number of second intersection points when there is an intersection between the line of sight and the bottom of the physical cloud layer. The embodiments of the present application do not limit the manner of obtaining the second intersection situation, and one possible obtaining manner is shown below.

[0168] In an exemplary embodiment, step 203 includes steps 2031 to 2032 (not shown in the figure).

[0169] Step 2031, obtain the bottom radius of the physical cloud layer.

[0170] As mentioned above, the bottom of the physical cloud layer is a lower spherical surface, and the size of the bottom of the physical cloud layer can be described by the bottom radius of the physical cloud layer. The embodiments of the present application do not limit the manner in which the electronic device obtains the bottom radius of the physical cloud layer. Exemplarily, the electronic device can obtain the bottom radius of the physical cloud layer input by the user, or the electronic device can calculate the bottom radius of the physical cloud layer, and the calculation method can be seen in the description of step 2011.

[0171] Step 2032, determine the second intersection situation based on the bottom radius of the physical cloud layer and the ray function for characterizing any line of sight.

[0172] In the embodiments of the present application, a circular function or an arc function corresponding to the bottom of the physical cloud layer can be determined based on the bottom radius of the physical cloud layer. For the sake of convenience of description, the circular function or the arc function, etc. is called the bottom function. In addition, the electronic device can determine the ray function for characterizing the line of sight. By solving the bottom function and the ray function, the second intersection situation is obtained, and the solving process will not be elaborated here.

[0173] Step 204, determine the exposure information corresponding to any line of sight based on the second intersection situation, the first position information of each candidate sampling point, and the reference density information.

[0174] As mentioned above, the second intersection situation can characterize whether the line of sight intersects with the bottom of the physical cloud layer. If the line of sight does not intersect with the bottom of the physical cloud layer, it means that there is no second intersection point between the line of sight and the bottom of the physical cloud layer. If the line of sight intersects with the bottom of the physical cloud layer, it means that there is at least one second intersection point between the line of sight and the bottom of the physical cloud layer.

[0175] Since the first intersection situation characterizes that there is a first intersection point between the line of sight and the top of the physical cloud layer, and the first intersection point is at least one, the following will be described in different cases according to the number of first intersection points, as shown in implementation manner B and implementation manner C.

[0176] In implementation mode B, there is one first intersection point. Step 204 includes steps B1 to B3 (not shown in the figure).

[0177] Step B1, if the second intersection situation indicates the existence of a second intersection point, then based on the second distance, the first position information of each candidate sampling point, and the reference density information, determine the exposure information corresponding to any line of sight. The second distance is the distance between the first intersection point and the second intersection point or the distance between the camera and the first intersection point.

[0178] In the embodiments of the present application, if there is one first intersection point and one second intersection point, it means that the line of sight passes through the physical cloud layer. In this case, the following several situations may exist. Situation 1, the camera is located inside the physical cloud layer. For example, the line of sight is tangent to the bottom of the physical cloud layer and passes through the top of the physical cloud layer. In this case, the distance between the camera and the first intersection point is the second distance. Situation 2, the camera is located below the physical cloud layer. For example, the camera passes through the bottom of the physical cloud layer and the top of the physical cloud layer in sequence. In this case, the distance between the first intersection point and the second intersection point is the second distance.

[0179] The second distance can be used as the reference distance. According to the operation principle of scattered light transmission by the integrator, based on the reference distance, the first position information of each candidate sampling point, and the reference density information, determine the exposure information corresponding to the line of sight. Among them, the integrator evaluates the distance density of physical units based on the Raymarching method to determine the exposure information of the line of sight in real time. The following will be combined with Figure 9 to elaborate on the determination process of the exposure information corresponding to the line of sight.

[0180] As Figure 9 shown, the line of sight of the camera passes through the bottom of the physical cloud layer and the top of the physical cloud layer in sequence, so that the line of sight of the camera passes through the physical cloud layer. There is a first intersection point between the line of sight of the camera and the top of the physical cloud layer, and a second intersection point between the line of sight of the camera and the bottom of the physical cloud layer. There is a cloud model in the physical cloud layer, and the line of sight may pass through the cloud model or may not pass through the cloud model.

[0181] The part of the line of sight located in the physical cloud layer, that is, the part between the first intersection point and the second intersection point in the line of sight, has at least one line-of-sight point. As Figure 9 shown, there are 8 line-of-sight points. It can be understood that if the line-of-sight point is located on the cloud model, this line-of-sight point can be a candidate sampling point. As Figure 9 shown, there are 5 candidate sampling points among the 8 line-of-sight points (that is, the 5 black solid circles on the line of sight shown in Figure 9 ). The distance between two adjacent line-of-sight points is called the line-of-sight marching distance.

[0182] For any line-of-sight point, there is at least one shadow point in the direction from the line-of-sight point to the light source. It can be understood that the intersection point between the line of sight and the direction from the line-of-sight point to the light source is the shadow point and also the line-of-sight point. If this intersection point lies on the cloud model, then this intersection point can also be a candidate sampling point. As Figure 9 shows that there are 6 shadow points in the directions from multiple line-of-sight points to the light source. The distance between two adjacent shadow points is called the shadow step distance.

[0183] In the embodiments of the present application, the integrator can determine the exposure information of the line of sight according to the following formula (1).

[0184]

[0185] Wherein, L(x, ω) represents the exposure information of the line of sight, that is, the exposure amount of the line of sight entering the camera. x represents the coordinate of the line-of-sight point, and ω represents the cosine value of the angle between the direction of the line of sight and the direction from the line-of-sight point to the light source. T r (x, x s ) represents the light transmittance of the occluder. Among them, Figure 9 the occluder is not shown. In practical applications, the occluder can be any object model other than the cloud model. For example, the occluder can be an airplane model, a bird model, etc. x s represents the position of the occluder. L(x s , ω) represents the exposure information corresponding to the occluder, that is, the exposure amount of the occluder entering the camera. Here, ω represents the cosine value of the angle between the direction of the occluder and the camera and the direction from the occluder to the light source. S represents the number of line-of-sight points, and dt represents the line-of-sight step distance. T r (x, x t ) represents the light transmittance of the line-of-sight point. x t represents the position of the shadow point located in the direction from the line-of-sight point to the light source. L S (x t , ω) represents the exposure information corresponding to the shadow point, that is, the exposure amount of the shadow point entering the camera. Here, ω represents the cosine value of the angle between the direction of the shadow point and the camera and the direction from the shadow point to the light source. σ S (x t ) represents the scattering coefficient corresponding to the shadow point. L e (x t , ω) represents the exposure amount of the self-luminescence of the shadow point entering the camera due to the self-luminescence property of the shadow point, which is simply referred to as the self-luminescence intensity of the shadow point. Here, ω represents the cosine value of the angle between the direction of the shadow point and the camera and the direction from the shadow point to the light source. σ a (x t ) represents the absorption coefficient of the medium where the shadow point is located.

[0186] In the above formula (1), L S (x t , ω) can be determined according to formula (2) shown below.

[0187]

[0188] Among them, Lights represents the number of light sources. For example, Figure 9 if only one light source is shown, then Lights = 1. P(ω, L) represents the phase of the L-th light source, that is, the alternating waveform change presented by the photon vibration when the light wave advances. ω represents the angular frequency of the phase. Vis(x, L) represents the occlusion rate of the L-th light source at the sight point x. L i (x, L) represents the illumination intensity of the L-th light source irradiating at the sight point x.

[0189] In the above formula (2), Vis(x, L) can be determined according to formula (3) shown below.

[0190] Vis(x, L) = Shadowmap(x, L) × VolumeShadow(x, L) Formula (3)

[0191] Among them, Shadowmap(x, L) represents the occlusion intensity caused by the shadow of other objects blocking the sight point x due to the irradiation of the L-th light source on other objects. VolumeShadow(x, L) represents the cumulative light source attenuation coefficient along the step from the sight point x to the L-th light source. For example, Figure 9 if there are 6 shadow points in the direction of each sight point pointing to the light source, then VolumeShadow(x, L) represents the cumulative light source attenuation coefficient of the 6 shadow points.

[0192] In the above formula (3), VolumeShadow(x, L) can be determined according to formula (4) shown below.

[0193] VolumeShadow(x, L) = T r (x, x L ) Formula (4)

[0194] Among them, T r (x, x L ) represents the light transmittance of the sight point x, and x L represents the position of the shadow point located in the direction of the sight point pointing to the L-th light source.

[0195] In addition, in the above formula (1), T r (x, x t ) can be determined according to formula (5) shown below.

[0196]

[0197] where exp represents the exponential function with the natural constant e as the base. σ t (x) represents the light transmittance obtained at the position of the shaded point x t dt represents the shaded step distance.

[0198] It should be noted that in the above formula (1), the number S of sight points multiplied by the sight step distance dt is equal to the reference distance. That is to say, the number of sight points and the sight step distance are determined based on the reference distance. Since the exposure information of the sight needs to be determined according to the above formulas (1) to (5), and the coordinates of each sight point and the coordinates of each shaded point are required, and the candidate sampling points can be sight points and shaded points, therefore, the coordinates of each sight point and the coordinates of each shaded point include the first position information of each candidate sampling point. That is to say, the first position information of each candidate sampling point needs to be used to calculate the exposure information of the sight. In addition, in the above formula (5), if the shaded point is a candidate sampling point, then the light transmittance σ t obtained at the position of the shaded point x t (x) is the reference density information of the candidate sampling point. Based on this, the reference density information of each candidate sampling point needs to be used to calculate the exposure information corresponding to the sight.

[0199] Step B2, if the second intersection situation indicates that there is no second intersection between any sight and the bottom of the physical cloud layer, then based on the distance between the camera and the first intersection point, the first position information and the reference density information of each candidate sampling point, determine the exposure information corresponding to any sight.

[0200] In the embodiments of the present application, if there is one first intersection point and no second intersection point, then there are the following several situations. Situation 1, the sight is tangent to the top of the physical cloud layer. In this case, the sight does not pass through the physical cloud layer, and the exposure information corresponding to the sight can be set to a set value (for example, 0), or, in other ways, determine the exposure information corresponding to the sight, and the determination method will not be elaborated here. Situation 2, the sight passes through the physical cloud layer. In this case, the camera is located inside the physical cloud layer, and the distance between the camera and the first intersection point can be used as the reference distance. Based on this reference distance, the first position information and the reference density information of each candidate sampling point, and based on the Raymarching method mentioned in step B1, determine the exposure information corresponding to the sight, and the determination method will not be elaborated.

[0201] Step B3, if the second intersection case indicates that there are two second intersections, determine the exposure information corresponding to any line of sight based on the distance between the first intersection and the third intersection that is closer to the first intersection among the two second intersections, the distance between the camera and the other second intersection except the third intersection among the two second intersections, the first position information of each candidate sampling point, and the reference density information.

[0202] In the embodiments of the present application, if there is one first intersection and two second intersections, it indicates that the line of sight passes through the physical cloud layer and the camera is located within the physical cloud layer. For example, the line of sight passes through the two second intersections and then the first intersection in sequence. The first of the two second intersections is closer to the camera, and the second second intersection is closer to the first intersection. The distance between the camera and the second intersection closer to the camera (i.e., the first second intersection) can be calculated, and the distance between the first intersection and the second intersection closer to the first intersection (i.e., the second second intersection) can be calculated. The sum of the two distances is used as the reference distance. Based on the reference distance, the first position information of each candidate sampling point, and the reference density information, the exposure information corresponding to the line of sight is determined in the way of Raymarching mentioned in Step B1, and the determination method will not be elaborated here.

[0203] In a possible implementation manner C, there are two first intersections. Step 204 includes Step C1 or Step C2 (not shown in the figure).

[0204] Step C1, if the second intersection case indicates that there is no second intersection or there is one second intersection between any line of sight and the bottom of the physical cloud layer, determine the exposure information corresponding to any line of sight based on the distance between the two first intersections, the first position information of each candidate sampling point, and the reference density information.

[0205] In the embodiments of the present application, if there are two first intersections and no second intersection, it indicates that the line of sight passes through the top of the physical cloud layer and does not pass through the bottom of the physical cloud layer, and the camera is located on the physical cloud layer. In this case, the distance between the two first intersections can be calculated, and this distance is used as the reference distance.

[0206] If there are two first intersections and one second intersection, it indicates that the line of sight passes through the top of the physical cloud layer and is tangent to the bottom of the physical cloud layer, and the camera is located on the physical cloud layer. In this case, the distance between one first intersection and the second intersection can be calculated, and the distance between the other first intersection and the second intersection can be calculated. The sum of the two distances, that is, the distance between the two first intersections, is used as the reference distance.

[0207] Next, based on the reference distance, the first position information of each candidate sampling point, and the reference density information, the exposure information corresponding to the line of sight is determined based on the Raymarching method mentioned in step B1. The determination method will not be elaborated here.

[0208] In step C2, if the second intersection situation indicates the existence of two second intersection points, then based on the distance between one first intersection point and the fourth intersection point that is closer to the one first intersection point among the two second intersection points, the distance between the other first intersection point and the other second intersection point among the two second intersection points except the fourth intersection point, the first position information of each candidate sampling point, and the reference density information, the exposure information corresponding to any line of sight is determined.

[0209] In the embodiment of the present application, if there are two first intersection points and two second intersection points, it means that the line of sight passes through the top of the physical cloud layer and passes through the bottom of the physical cloud layer, and the camera is located on the physical cloud layer. In this case, the line of sight passes through the first intersection point, the second intersection point, the second intersection point, and the first intersection point in sequence. The distance between one first intersection point and the fourth intersection point that is closer to the one first intersection point among the two second intersection points (for example, the first first intersection point and the first second intersection point) can be calculated, and the distance between the other first intersection point and the other second intersection point among the two second intersection points except the fourth intersection point (for example, the second second intersection point and the second first intersection point) can be calculated. The sum of the two distances is used as the reference distance. Next, based on the reference distance, the first position information of each candidate sampling point, and the reference density information, the exposure information corresponding to the line of sight is determined based on the Raymarching method mentioned in step B1. The determination method will not be elaborated here.

[0210] In addition to the implementation methods B and C involved above, there is also a possible implementation method D as shown below. In implementation method D, step 204 includes step D1 or step D2 (not shown in the figure).

[0211] In step D1, the third density information of multiple candidate sampling points and the second position information of each candidate sampling point relative to the physical cloud layer are obtained.

[0212] The third density information of the candidate sampling point is used to characterize the aggregation degree of the candidate sampling point. The greater the third density information, the higher the aggregation degree of the candidate sampling point. The second position information of the candidate sampling point is used to characterize the position of the candidate sampling point relative to the physical cloud layer where the candidate sampling point is located, that is, the position of the candidate sampling point within the physical cloud layer. Optionally, the second position information of the candidate sampling point includes the x coordinate, y coordinate, and z coordinate of the candidate sampling point. The position of the candidate sampling point on the horizontal plane is characterized by the x coordinate and y coordinate, and the height or normalized height of the candidate sampling point within the physical cloud layer is characterized by the z coordinate.

[0213] Exemplarily, when the z - coordinate of a candidate sampling point represents the normalized height of the candidate sampling point within the physical cloud layer, the second position information of the candidate sampling point is also referred to as the normalized position information of the candidate sampling point within the physical cloud layer. As mentioned above, the distance between the candidate sampling point and the bottom of the physical cloud layer is the height of the candidate sampling point within the physical cloud layer, and the distance between the top and the bottom of the physical cloud layer is called the total height of the physical cloud layer. The ratio of the height of the candidate sampling point within the physical cloud layer to the total height of the physical cloud layer is the normalized height of the candidate sampling point within the physical cloud layer.

[0214] The electronic device can obtain the third density information and the second position information of each candidate sampling point. The embodiments of the present application do not limit the acquisition method. Exemplarily, the electronic device can obtain the third density information and the second position information of each candidate sampling point input by the user. Or, the electronic device can calculate the third density information and the second position information of each candidate sampling point. The calculation method can be seen in the content from step 2021 to step 2024. The implementation principles of the two are similar and will not be elaborated here.

[0215] Step D2: Based on the second intersection situation, the first position information, the second position information, the third density information, and the reference density information of each candidate sampling point, determine the exposure information corresponding to any line of sight.

[0216] In the embodiments of the present application, the x - coordinate and y - coordinate in the first position information of the candidate sampling point are the same as the x - coordinate and y - coordinate in the second position information of the candidate sampling point, but the z - coordinate in the first position information of the candidate sampling point is different from the z - coordinate in the second position information of the candidate sampling point. The candidate sampling points within the physical cloud layer have two sets of heights: the height or normalized height within the physical cloud layer; the height or normalized height within the virtual sub - layer. These two sets of height systems co - exist and can share the same Raymarching channel.

[0217] As Figure 10 shown, the physical cloud layer is located above the surface of the planet, and the line of sight of the camera passes through the physical cloud layer and points to the light source. There are cloud models distributed within the physical cloud layer, and the physical cloud layer includes virtual sub - layer 1 and virtual sub - layer 2. The candidate sampling points located on the cloud model can correspond to the normalized height of the physical cloud layer, or the normalized height of virtual sub - layer 1 or virtual sub - layer 2, or both the normalized height of the physical cloud layer and the normalized height of virtual sub - layer 1 or virtual sub - layer 2.

[0218] Since the height system of the physical cloud layer and the height system of the virtual sub-layer coexist and share the same Raymarching channel, the exposure information corresponding to the line of sight can be determined based on the second intersection situation, the first position information, the second position information, the third density information, and the reference density information of each candidate sampling point in the manner of Raymarching mentioned in step B1, and the determination method will not be elaborated here.

[0219] Exemplarily, on the one hand, the first exposure information corresponding to the line of sight can be calculated based on the second intersection situation, the first position information of each candidate sampling point, and the reference density information in the manner of Raymarching mentioned in step B1. On the other hand, the second exposure information corresponding to the line of sight can be calculated based on the second intersection situation, the second position information of each candidate sampling point, and the third density information in the manner of Raymarching mentioned in step B1. The first exposure information and the second exposure information corresponding to the line of sight are summed, averaged, or weighted calculated, etc., to obtain the exposure information corresponding to the line of sight.

[0220] In the embodiments of the present application, by determining the second height of each candidate sampling point relative to the physical cloud layer, the distribution of the cloud model within the physical cloud layer is controlled. Since the physical cloud layer includes virtual sub-layers, by determining the second height of the candidate sampling points, the modeling distribution of the cloud model within the same virtual sub-layer, different virtual sub-layers, between virtual sub-layers, across virtual sub-layers, etc. can be realized, such as nimbostratus or cumulonimbus clouds. Through the coexistence of the height system of the physical cloud layer and the height system of the virtual sub-layer, the distribution of some cloud models within the virtual sub-layer, some cloud models across virtual sub-layers, and some cloud models between virtual sub-layers is controlled, etc., so that the cloud models between different virtual sub-layers can be well integrated together, making the distribution of the cloud model approximate to the cloud distribution in nature and improving the fidelity. As Figure 11 shown, the rendered image includes clouds distributed in the upper layer, clouds distributed in the lower layer, and clouds distributed across layers.

[0221] Step 205: Determine the rendered image of the cloud model based on the exposure information corresponding to each line of sight.

[0222] In the embodiments of the present application, the exposure information corresponding to any line of sight is used to reflect the light intensity of the line of sight entering the camera. The pixel value of the pixel point corresponding to the line of sight on the rendered image can be determined based on the exposure information corresponding to the line of sight, and the determination method will not be elaborated here. Based on this, it is possible to determine the pixel values of each pixel point on the rendered image based on the exposure information corresponding to each line of sight, thereby obtaining the rendered image of the cloud model and realizing the rendering of the cloud model to obtain the rendered image of the cloud model.

[0223] It should be noted that the information involved in this application (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.), and signals are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards in the relevant regions. For example, the radius of the planet and the thickness of the physical cloud layer involved in this application are obtained under full authorization.

[0224] In the above method, the physical cloud layer includes at least one virtual sub-layer. By configuring the first position information of each candidate sampling point included in the cloud model relative to the virtual sub-layer where it is located, the distribution of the cloud model within the virtual sub-layer is controlled, that is, the hierarchical distribution of the cloud model is controlled. In addition, first, the first intersection situation between the line of sight of the camera and the top of the physical cloud layer is obtained. When the first intersection situation indicates that there is a first intersection point between the line of sight and the top of the physical cloud layer, the second intersection situation between the line of sight of the camera and the bottom of the physical cloud layer is obtained, and the exposure information corresponding to the line of sight is determined based on the second intersection situation. The exposure information corresponding to the line of sight is determined through the two intersection situations between the line of sight of the camera and the physical cloud layer, reducing the calculation amount and improving the determination efficiency of the exposure information, so that the rendering image can be quickly determined based on the exposure information corresponding to the line of sight, improving the rendering efficiency.

[0225] The above describes the rendering method of the cloud model provided by the embodiments of this application from the perspective of method steps. The following will be comprehensively described in combination with Figure 12 As shown in Figure 12 The rendering process of the cloud model in the embodiments of this application includes steps 1201 to 1205 as follows.

[0226] Step 1201: Obtain the horizontal control parameter and vertical control parameter input by the user.

[0227] Step 1202: Determine the cloud models distributed within the virtual sub-layer, between the virtual sub-layers, and across the virtual sub-layers based on the horizontal control parameter and the vertical control parameter.

[0228] Step 1203: Obtain the first intersection situation between multiple lines of sight of the camera and the top of the physical cloud layer.

[0229] Step 1204: Based on the fact that the first intersection situation indicates that there is a first intersection point between any line of sight and the top of the physical cloud layer, obtain the second intersection situation between any line of sight and the bottom of the physical cloud layer.

[0230] Step 1205: Determine the rendering image of the cloud model based on the second intersection situation.

[0231] It can be understood that the implementation manners of steps 1201 to 1205 can be seen in the relevantFigure 2 The description is not repeated here. According to Figure 12 the rendering steps of the cloud model shown, not only can the cloud rendering images distributed within the virtual sub-layers be rendered, but also the cloud rendering images distributed between the virtual sub-layers can be rendered, and even the cloud rendering images distributed across the virtual sub-layers can be rendered, and the rendering efficiency is relatively high.

[0232] As Figure 13 shown, from the perspective between the cloud layers, the cloud includes the cloud distributed in the upper layer and the cloud distributed in the lower layer, realizing the rendering of the cloud rendering image with the characteristic of layered distribution. Optionally, the upper layer and the lower layer correspond to two virtual sub-layers, the thickness of each virtual sub-layer is 1 kilometre (km), the distance between the two virtual sub-layers is 1.5 kilometres, and the distance between the virtual sub-layer corresponding to the lower layer and the surface of the planet is 0.2 kilometres. According to Figure 12 the rendering steps of the cloud model shown, the rendering of Figure 13 the rendering image shown only takes 3 to 5 minutes.

[0233] As Figure 14 shown, for the cloud distributed in the upper layer and the cloud distributed in the lower layer, from the top-down perspective of the upper layer, the cloud includes the cloud distributed in the upper layer and the cloud distributed in the lower layer, still having the characteristic of layered distribution. Optionally, the upper layer and the lower layer correspond to two virtual sub-layers, the thickness of each virtual sub-layer is 2 kilometres, the distance between the two virtual sub-layers is 1.5 kilometres, and the distance between the virtual sub-layer corresponding to the lower layer and the surface of the planet is 0.6 kilometres.

[0234] As Figure 15 shown, when looking at the cloud from the perspective between the cloud layers with a long viewing distance, the cloud includes the cloud distributed in the upper layer and the cloud distributed in the lower layer. As Figure 16 shown, when looking at the cloud from the perspective between the cloud layers with a medium viewing distance, the cloud includes the cloud distributed in the upper layer and the cloud distributed in the lower layer. As Figure 17 shown, when looking at the cloud from the perspective between the cloud layers with a short viewing distance, the cloud includes the cloud distributed in the lower layer. As Figure 18 shown, when looking at the cloud from the upward perspective under the cloud layer with a long viewing distance, the cloud includes the cloud distributed in the upper layer and the cloud distributed in the lower layer. Among them, the long viewing distance, the medium viewing distance and the short viewing distance are relative viewing distances, and the values among the three are not limited, as long as the long viewing distance is greater than the medium viewing distance, and the medium viewing distance is greater than the short viewing distance.

[0235] In addition, different wind fields can be set for different cloud layers to control different cloud layers to move at different moving speeds. As Figure 19 shown, there are cloud rendering images moving at four different moving speeds.

[0236] The solution of the embodiment of the present application has the characteristic of high flexibility. By layering the physical cloud layer to obtain virtual sub-layers, it is possible to quickly construct a cloud model with a layered distribution characteristic through a small number of control parameters, and render a rendered image of the cloud that can restore the cloud with a layered distribution characteristic in nature through a renderer. Since the number of physical cloud layers is not increased, the relative position between the camera and the physical cloud layer can be determined only by the two intersection situations of the physical cloud layer and the camera line of sight, so that the exposure information can be quickly determined, and the rendered image of the cloud model can be quickly rendered, completing the cloud landscaping of the virtual scene, and enabling the cloud to run at an interactive rate while ensuring a certain fidelity of the cloud.

[0237] By Figures 13 to 19 It can be seen that the rendering method of the embodiment of the present application can be applied to render a rendered image of the cloud in the distant sky viewed from the ground perspective, a rendered image of the continuous sea of clouds viewed from the top-down perspective, and a rendered image of the sea of clouds viewed when an aircraft shuttles through the cloud layer, realizing the process of viewing the rendered image of the sea of clouds at different stages from starting to climb gradually from the ground until contacting the cloud layer and then jumping over the top of the cloud layer, and achieving a good rendering effect at multiple viewing distances and viewing angles. In addition, since only the two intersection situations of the physical cloud layer and the camera line of sight need to be judged during the rendering process, the calculation amount is reduced, so that the solution of the embodiment of the present application can be deployed in multiple product devices such as personal computers (PCs), mainframes, etc., and has a good rendering effect and performance when using these product devices to render the rendered image of the sea of clouds.

[0238] The solution provided by the embodiment of the present application can be applied to business scenarios involving cloud model rendering. For example, the business scenarios include animation business, film and television business, game business, etc. Hereinafter, the game business of game types such as open world will be taken as an example for elaboration.

[0239] On the one hand, according to the solution of the embodiment of the present application, a cloud model with a layered distribution, cross-layer distribution, and high fidelity can be quickly constructed through a small number of control parameters, realizing the rapid completion of the cloud landscaping of the virtual scene, which is beneficial to reducing the time required for game development and game maintenance and improving the authenticity of the game scene.

[0240] On the other hand, according to the solution of the embodiment of the present application, the relative position between the camera and the physical cloud layer can be determined only by the two intersection situations of the physical cloud layer and the camera line of sight, so as to render the rendered image of the cloud model, reducing the calculation amount of the rendering process, enabling games of game types such as open world to run relatively smoothly on electronic devices with poor performance, and reducing the hardware conditions required for game operation.

[0241] Figure 20The following is a schematic structural diagram of a rendering device for a cloud model provided by an embodiment of the present application. As Figure 20 shown, the device includes:

[0242] An acquisition module 2001, configured to acquire a first intersection situation between a plurality of sight lines of a camera and the top of a physical cloud layer, where the physical cloud layer includes at least one virtual sub-layer, the physical cloud layer is used to distribute the cloud model, and the camera is used to perform virtual imaging on the cloud model;

[0243] The acquisition module 2001 is further configured to acquire reference density information of a plurality of candidate sampling points included in the cloud model and first position information of each candidate sampling point relative to the virtual sub-layer where it is located;

[0244] The acquisition module 2001 is further configured to, for any one of the sight lines of the camera, if the first intersection situation indicates that there is a first intersection point between any one of the sight lines and the top of the physical cloud layer, acquire a second intersection situation between any one of the sight lines and the bottom of the physical cloud layer;

[0245] A determination module 2002, configured to determine exposure information corresponding to any one of the sight lines based on the second intersection situation, the first position information, and the reference density information of each candidate sampling point;

[0246] The determination module 2002 is further configured to determine a rendered image of the cloud model based on the exposure information corresponding to each sight line.

[0247] In a possible implementation manner, the first position information of the candidate sampling point includes the horizontal position of the candidate sampling point on the horizontal plane and the first height of the candidate sampling point relative to the virtual sub-layer where it is located;

[0248] The acquisition module 2001 is configured to acquire a horizontal control parameter and a vertical control parameter, where the horizontal control parameter is used to control the styling distribution of the cloud model on the horizontal plane, and the vertical control parameter is used to control the styling distribution of the cloud model on the vertical plane; based on the horizontal control parameter, determine the horizontal position and first density information of a plurality of candidate sampling points; based on the vertical control parameter, determine the first height and second density information of a plurality of candidate sampling points; based on the first density information and the second density information of a plurality of candidate sampling points, determine the reference density information of a plurality of candidate sampling points.

[0249] In a possible implementation, an acquisition module 2001 is configured to acquire noise data of a plurality of initial points; adjust the noise data of the plurality of initial points through a horizontal control parameter to obtain a density distribution mask, where the density distribution mask is used to reflect the horizontal positions and density information of the respective initial points; for the density information of any one of the initial points in the density distribution mask, if the density information of any one of the initial points meets the information condition, determine the horizontal position of any one of the initial points as the horizontal position of a candidate sampling point, and determine the density information of any one of the initial points as the first density information of a candidate point.

[0250] In a possible implementation, an acquisition module 2001 is configured to numerically adjust the horizontal control parameter to obtain an adjusted horizontal control parameter; based on the adjusted horizontal control parameter, adjust the noise data of the plurality of initial points to obtain a density distribution mask.

[0251] In a possible implementation, the vertical control parameter includes at least one of the following:

[0252] A bottom control parameter for controlling the bottom shape of the cloud model;

[0253] A top control parameter for controlling the top shape of the cloud model;

[0254] A transition position control parameter for controlling the transition from the bottom shape of the cloud model to the top shape of the cloud model;

[0255] A frequency parameter for controlling the occurrence frequency of the edge details of the cloud model;

[0256] A size parameter for controlling the size of the edge details of the cloud model;

[0257] A transition mode control parameter for controlling the transition from the bottom shape of the cloud model to the top shape of the cloud model.

[0258] In a possible implementation, an acquisition module 2001 is configured to acquire the top radius of the physical cloud layer; for any line of sight of the camera, determine a first intersection situation based on the top radius of the physical cloud layer and a ray function for characterizing any line of sight.

[0259] In a possible implementation, an acquisition module 2001 is configured to acquire the radius of the planet, the thickness of the physical cloud layer, and a first distance between the bottom of the physical cloud layer and the surface of the planet; determine the bottom radius of the physical cloud layer based on the radius of the planet and the first distance; determine the top radius of the physical cloud layer based on the bottom radius of the physical cloud layer and the thickness of the physical cloud layer.

[0260] In a possible implementation, an obtaining module 2001 is configured to obtain the bottom radius of a physical cloud layer; and determine a second intersection situation based on the bottom radius of the physical cloud layer and a ray function for characterizing any line of sight.

[0261] In a possible implementation, there is one first intersection point.

[0262] A determining module 2002 is configured to, if the second intersection situation indicates that there is no second intersection point between any line of sight and the bottom of the physical cloud layer, determine the exposure information corresponding to any line of sight based on the distance between the camera and the first intersection point, the first position information of each candidate sampling point, and the reference density information; if the second intersection situation indicates that there is one second intersection point, determine the exposure information corresponding to any line of sight based on the second distance, the first position information of each candidate sampling point, and the reference density information, where the second distance is the distance between the first intersection point and the second intersection point or the distance between the camera and the first intersection point; if the second intersection situation indicates that there are two second intersection points, determine the exposure information corresponding to any line of sight based on the distance between the first intersection point and a third intersection point that is closer to the first intersection point among the two second intersection points, the distance between the camera and the other second intersection point except the third intersection point among the two second intersection points, the first position information of each candidate sampling point, and the reference density information.

[0263] In a possible implementation, there are two first intersection points.

[0264] A determining module 2002 is configured to, if the second intersection situation indicates that there is no second intersection point or there is one second intersection point between any line of sight and the bottom of the physical cloud layer, determine the exposure information corresponding to any line of sight based on the distance between the two first intersection points, the first position information of each candidate sampling point, and the reference density information; if the second intersection situation indicates that there are two second intersection points, determine the exposure information corresponding to any line of sight based on the distance between one first intersection point and a fourth intersection point that is closer to the one first intersection point among the two second intersection points, the distance between the other first intersection point and the other second intersection point except the fourth intersection point among the two second intersection points, the first position information of each candidate sampling point, and the reference density information.

[0265] In a possible implementation, a determining module 2002 is configured to obtain the third density information of multiple candidate sampling points and the second position information of each candidate sampling point relative to the physical cloud layer; and determine the exposure information corresponding to any line of sight based on the second intersection situation, the first position information, the second position information, the third density information, and the reference density information of each candidate sampling point.

[0266] In the above device, the physical cloud layer includes at least one virtual sub-layer. By configuring the first position information of each candidate sampling point included in the cloud model relative to the virtual sub-layer where it is located, the distribution of the cloud model within the virtual sub-layer is controlled, that is, the hierarchical distribution of the cloud model is controlled. In addition, first obtain the first intersection situation between the line of sight of the camera and the top of the physical cloud layer. When the first intersection situation indicates that there is a first intersection point between the line of sight and the top of the physical cloud layer, obtain the second intersection situation between the line of sight of the camera and the bottom of the physical cloud layer, and determine the exposure information corresponding to the line of sight based on the second intersection situation. By determining the exposure information corresponding to the line of sight through the two intersection situations between the line of sight of the camera and the physical cloud layer, the calculation amount is reduced, the determination efficiency of the exposure information is improved, so that the rendering image can be quickly determined based on the exposure information corresponding to the line of sight, and the rendering efficiency is improved.

[0267] It should be understood that when the above Figure 20 provided device realizes its functions, only the above division of each functional module is used for illustration. In actual applications, the above functions can be assigned to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the method embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be elaborated here.

[0268] Figure 21 The structural block diagram of a terminal device 2100 provided by an exemplary embodiment of the present application is shown. The terminal device 2100 includes: a processor 2101 and a memory 2102.

[0269] The processor 2101 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The processor 2101 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 2101 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 2101 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 2101 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0270] The memory 2102 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 2102 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 2102 is used to store at least one computer program, and the at least one computer program is used to be executed by the processor 2101 to implement the rendering method of the cloud model provided in the method embodiments of the present application.

[0271] In some embodiments, the terminal device 2100 may further optionally include: a peripheral device interface 2103 and at least one peripheral device. The processor 2101, the memory 2102, and the peripheral device interface 2103 may be connected by a bus or signal lines. Each peripheral device may be connected to the peripheral device interface 2103 through a bus, signal lines, or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 2104, a display screen 2105, a camera assembly 2106, an audio circuit 2107, and a power supply 2108.

[0272] The peripheral device interface 2103 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 2101 and the memory 2102. In some embodiments, the processor 2101, the memory 2102, and the peripheral device interface 2103 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 2101, the memory 2102, and the peripheral device interface 2103 can be implemented on a separate chip or circuit board, and this embodiment does not limit this.

[0273] The radio frequency circuit 2104 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 2104 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 2104 converts an electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 2104 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so on. The radio frequency circuit 2104 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, generations of mobile communication networks (2G, 3G, 4G, and 5G), a wireless local area network, and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 2104 may further include a circuit related to NFC (Near Field Communication), and this application does not limit this.

[0274] The display screen 2105 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 2105 is a touch display screen, the display screen 2105 also has the ability to collect touch signals on or above the surface of the display screen 2105. The touch signals can be input to the processor 2101 as control signals for processing. At this time, the display screen 2105 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 2105, which is provided on the front panel of the terminal device 2100; in other embodiments, there may be at least two display screens 2105, which are respectively provided on different surfaces of the terminal device 2100 or are in a folding design; in other embodiments, the display screen 2105 may be a flexible display screen, which is provided on the curved surface or the folding surface of the terminal device 2100. Even, the display screen 2105 can also be set to an irregular non-rectangular shape, that is, a special-shaped screen. The display screen 2105 can be prepared using materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0275] The camera module 2106 is used to collect images or videos. Optionally, the camera module 2106 includes a front camera and a rear camera. Generally, the front camera is provided on the front panel of the terminal, and the rear camera is provided on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera respectively, to implement functions such as background blurring by fusing the main camera and the depth-of-field camera, panoramic shooting by fusing the main camera and the wide-angle camera, and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera module 2106 may also include a flash. The flash can be a single-color temperature flash or a two-color temperature flash. A two-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation under different color temperatures.

[0276] The audio circuit 2107 may include a microphone and a speaker. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into electrical signals for input to the processor 2101 for processing, or input to the radio frequency circuit 2104 to achieve voice communication. For the purpose of stereo collection or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal device 2100. The microphone may also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signal from the processor 2101 or the radio frequency circuit 2104 into sound waves. The speaker may be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for uses such as ranging. In some embodiments, the audio circuit 2107 may further include a headphone jack.

[0277] The power supply 2108 is used to supply power to each component in the terminal device 2100. The power supply 2108 may be alternating current, direct current, a primary battery or a rechargeable battery. When the power supply 2108 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charging technology.

[0278] In some embodiments, the terminal device 2100 further includes one or more sensors 2109. The one or more sensors 2109 include but are not limited to: an acceleration sensor 2111, a gyroscope sensor 2112, a pressure sensor 2113, an optical sensor 2114, and a proximity sensor 2115.

[0279] The acceleration sensor 2111 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established with the terminal device 2100. For example, the acceleration sensor 2111 can be used to detect the components of the gravitational acceleration on the three coordinate axes. The processor 2101 can control the display screen 2105 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 2111. The acceleration sensor 2111 can also be used for collecting game or user's motion data.

[0280] The gyroscope sensor 2112 can detect the body direction and rotation angle of the terminal device 2100. The gyroscope sensor 2112 can cooperate with the acceleration sensor 2111 to collect the 3D actions of the user on the terminal device 2100. According to the data collected by the gyroscope sensor 2112, the processor 2101 can implement the following functions: motion sensing (such as changing the UI according to the user's tilting operation), image stabilization during shooting, game control, and inertial navigation.

[0281] The pressure sensor 2113 can be disposed on the side frame of the terminal device 2100 and / or the lower layer of the display screen 2105. When the pressure sensor 2113 is disposed on the side frame of the terminal device 2100, it can detect the holding signal of the user on the terminal device 2100, and the processor 2101 performs left / right hand recognition or quick operation according to the holding signal collected by the pressure sensor 2113. When the pressure sensor 2113 is disposed on the lower layer of the display screen 2105, the processor 2101 controls the operable controls on the UI interface according to the pressure operation of the user on the display screen 2105. The operable controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0282] The optical sensor 2114 is used to collect the ambient light intensity. In one embodiment, the processor 2101 can control the display brightness of the display screen 2105 according to the ambient light intensity collected by the optical sensor 2114. Specifically, when the ambient light intensity is high, the display brightness of the display screen 2105 is increased; when the ambient light intensity is low, the display brightness of the display screen 2105 is decreased. In another embodiment, the processor 2101 can also dynamically adjust the shooting parameters of the camera module 2106 according to the ambient light intensity collected by the optical sensor 2114.

[0283] The proximity sensor 2115, also known as a distance sensor, is usually disposed on the front panel of the terminal device 2100. The proximity sensor 2115 is used to collect the distance between the user and the front of the terminal device 2100. In one embodiment, when the proximity sensor 2115 detects that the distance between the user and the front of the terminal device 2100 is gradually decreasing, the processor 2101 controls the display screen 2105 to switch from the lit state to the off state; when the proximity sensor 2115 detects that the distance between the user and the front of the terminal device 2100 is gradually increasing, the processor 2101 controls the display screen 2105 to switch from the off state to the lit state.

[0284] Those skilled in the art can understand that Figure 21 the structure shown in does not limit the terminal device 2100, and it may include more or fewer components than shown in the figure, or combine certain components, or adopt different component arrangements.

[0285] Figure 22A structural schematic diagram of the server provided by the embodiment of the present application. The server 2200 may vary greatly due to different configurations or performances, and may include one or more processors 2201 and one or more memories 2202. Among them, at least one computer program is stored in the one or more memories 2202, and the at least one computer program is loaded and executed by the one or more processors 2201 to implement the rendering method of the cloud model provided by each of the above method embodiments. Exemplarily, the processor 2201 is a CPU. Of course, the server 2200 may also have components such as a wired or wireless network interface, a keyboard, and an input / output interface for input and output. The server 2200 may also include other components for implementing the functions of the device, which will not be elaborated here.

[0286] In an exemplary embodiment, a computer-readable storage medium is further provided. At least one computer program is stored in the storage medium, and the at least one computer program is loaded and executed by a processor to enable an electronic device to implement any one of the above rendering methods of the cloud model.

[0287] Optionally, the above computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0288] In an exemplary embodiment, a computer program is further provided. The computer program is at least one, and the at least one computer program is loaded and executed by a processor to enable an electronic device to implement any one of the above rendering methods of the cloud model.

[0289] In an exemplary embodiment, a computer program product is further provided. At least one computer program is stored in the computer program product, and the at least one computer program is loaded and executed by a processor to enable an electronic device to implement any one of the above rendering methods of the cloud model.

[0290] It should be understood that "a plurality" mentioned herein refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0291] The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0292] The above are only exemplary embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included within the protection scope of the present application.

Claims

1. A rendering method of a cloud model, characterized in that, the method includes: obtaining a first intersection situation between a plurality of sight lines of a camera and the top of a physical cloud layer, the physical cloud layer including at least one virtual sub-layer, the physical cloud layer being used for distributing the cloud model, and the camera being used for performing virtual imaging on the cloud model; obtaining reference density information of a plurality of candidate sampling points included in the cloud model and first position information of each candidate sampling point relative to the virtual sub-layer where it is located; for any one of the sight lines of the camera, if the first intersection situation indicates that there is a first intersection point between the any one of the sight lines and the top of the physical cloud layer, then obtaining a second intersection situation between the any one of the sight lines and the bottom of the physical cloud layer; determining exposure information corresponding to the any one of the sight lines based on the second intersection situation, the first position information and the reference density information of each candidate sampling point; determining a rendered image of the cloud model based on the exposure information corresponding to each sight line.

2. The method according to claim 1, characterized in that, the first position information of the candidate sampling point includes the horizontal position of the candidate sampling point on the horizontal plane and the first height of the candidate sampling point relative to the virtual sub-layer where it is located; the obtaining reference density information of a plurality of candidate sampling points included in the cloud model and first position information of each candidate sampling point relative to the virtual sub-layer where it is located includes: obtaining a horizontal control parameter and a vertical control parameter, the horizontal control parameter being used for controlling the shape distribution of the cloud model on the horizontal plane, and the vertical control parameter being used for controlling the shape distribution of the cloud model on the vertical plane; determining the horizontal positions and first density information of the plurality of candidate sampling points based on the horizontal control parameter; determining the first heights and second density information of the plurality of candidate sampling points based on the vertical control parameter; determining the reference density information of the plurality of candidate sampling points based on the first density information and the second density information of the plurality of candidate sampling points.

3. The method according to claim 2, characterized in that, the determining the horizontal positions and first density information of the plurality of candidate sampling points based on the horizontal control parameter includes: obtaining noise data of a plurality of initial points; adjusting the noise data of the plurality of initial points by the horizontal control parameter to obtain a density distribution mask, the density distribution mask being used for reflecting the horizontal positions and density information of each initial point; for the density information of any one initial point in the density distribution mask, if the density information of the any one initial point meets the information condition, determining the horizontal position of the any one initial point as the horizontal position of a candidate sampling point, and determining the density information of the any one initial point as the first density information of the one candidate point.

4. The method according to claim 3, characterized in that, the adjusting the noise data of the plurality of initial points by the horizontal control parameter to obtain a density distribution mask includes: performing numerical adjustment on the horizontal control parameter to obtain an adjusted horizontal control parameter; Adjust the noise data of the multiple initial points based on the adjusted horizontal control parameter to obtain a density distribution mask.

5. The method according to claim 2, wherein, the vertical control parameter includes at least one of the following: a bottom control parameter for controlling the bottom shape of the cloud model; a top control parameter for controlling the top shape of the cloud model; a transition position control parameter for controlling the transition from the bottom shape of the cloud model to the top shape of the cloud model; a frequency parameter for controlling the occurrence frequency of the edge details of the cloud model; a size parameter for controlling the size of the edge details of the cloud model; a transition mode control parameter for controlling the transition from the bottom shape of the cloud model to the top shape of the cloud model.

6. The method according to claim 1, wherein, the obtaining of the first intersection situation between the multiple sight lines of the camera and the top of the physical cloud layer includes: obtaining the top radius of the physical cloud layer; for any one of the sight lines of the camera, determining the first intersection situation based on the top radius of the physical cloud layer and the ray function for characterizing the any one sight line.

7. The method according to claim 6, wherein, the obtaining of the top radius of the physical cloud layer includes: obtaining the radius of the planet, the thickness of the physical cloud layer, and the first distance between the bottom of the physical cloud layer and the surface of the planet; determining the bottom radius of the physical cloud layer based on the radius of the planet and the first distance; determining the top radius of the physical cloud layer based on the bottom radius of the physical cloud layer and the thickness of the physical cloud layer.

8. The method according to claim 1, wherein, the obtaining of the second intersection situation between the any one sight line and the bottom of the physical cloud layer includes: obtaining the bottom radius of the physical cloud layer; determining the second intersection situation based on the bottom radius of the physical cloud layer and the ray function for characterizing the any one sight line.

9. The method according to any one of claims 1 to 8, wherein, there is one first intersection point; the determining of the exposure information corresponding to the any one sight line based on the second intersection situation, the first position information of each candidate sampling point, and the reference density information includes: if the second intersection situation indicates that there is no second intersection point between the any one sight line and the bottom of the physical cloud layer, then determining the exposure information corresponding to the any one sight line based on the distance between the camera and the first intersection point, the first position information of each candidate sampling point, and the reference density information; if the second intersection situation indicates that there is one second intersection point, then determining the exposure information corresponding to the any one sight line based on the second distance, the first position information of each candidate sampling point, and the reference density information, where the second distance is the distance between the first intersection point and the second intersection point or the distance between the camera and the first intersection point. If the second intersection situation indicates the existence of two second intersection points, then based on the distance between the first intersection point and a third intersection point that is closer to the first intersection point among the two second intersection points, the distance between the camera and the other second intersection point among the two second intersection points excluding the third intersection point, the first position information and the reference density information of each candidate sampling point, determine the exposure information corresponding to any one of the lines of sight.

10. The method according to any one of claims 1 to 8, wherein, there are two first intersection points; the determining the exposure information corresponding to any one of the lines of sight based on the second intersection situation, the first position information and the reference density information of each candidate sampling point includes: If the second intersection situation indicates that there is no second intersection point or there is one second intersection point between any one of the lines of sight and the bottom of the physical cloud layer, then based on the distance between the two first intersection points, the first position information and the reference density information of each candidate sampling point, determine the exposure information corresponding to any one of the lines of sight; If the second intersection situation indicates the existence of two second intersection points, then based on the distance between one first intersection point and a fourth intersection point that is closer to the one first intersection point among the two second intersection points, the distance between the other first intersection point and the other second intersection point among the two second intersection points excluding the fourth intersection point, the first position information and the reference density information of each candidate sampling point, determine the exposure information corresponding to any one of the lines of sight.

11. The method according to any one of claims 1 to 8, wherein, the determining the exposure information corresponding to any one of the lines of sight based on the second intersection situation, the first position information and the reference density information of each candidate sampling point includes: obtain the third density information of the multiple candidate sampling points and the second position information of each candidate sampling point relative to the physical cloud layer; Based on the second intersection situation, the first position information, the second position information, the third density information and the reference density information of each candidate sampling point, determine the exposure information corresponding to any one of the lines of sight.

12. A rendering device for a cloud model, wherein, the device includes: an acquisition module, configured to acquire a first intersection situation between multiple lines of sight of a camera and the top of a physical cloud layer, the physical cloud layer includes at least one virtual sub-layer, the physical cloud layer is used for distributing the cloud model, and the camera is used for virtual imaging of the cloud model; the acquisition module is further configured to acquire the reference density information of multiple candidate sampling points included in the cloud model and the first position information of each candidate sampling point relative to the virtual sub-layer where it is located; the acquisition module is further configured to, for any one of the lines of sight of the camera, if the first intersection situation indicates the existence of a first intersection point between any one of the lines of sight and the top of the physical cloud layer, acquire the second intersection situation between any one of the lines of sight and the bottom of the physical cloud layer; a determination module, configured to determine the exposure information corresponding to any one of the lines of sight based on the second intersection situation, the first position information and the reference density information of each candidate sampling point; The determining module is further configured to determine a rendered image of the cloud model based on the exposure information corresponding to each line of sight.

13. An electronic device, characterized in that, the electronic device includes a processor and a memory, and at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to enable the electronic device to implement the cloud model rendering method according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that, at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to enable an electronic device to implement the cloud model rendering method according to any one of claims 1 to 11.

15. A computer program product, characterized in that, at least one computer program is stored in the computer program product, and the at least one computer program is loaded and executed by a processor to enable an electronic device to implement the cloud model rendering method according to any one of claims 1 to 11.

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