CN114432695A - Particle system rendering method, apparatus, electronic device and readable storage medium - Google Patents

Abstract

The embodiment of the invention discloses a particle system rendering method, a particle system rendering device, electronic equipment and a readable storage medium, wherein the particle system rendering method comprises the following steps: classifying and storing the attributes of the particles in the preset particle cluster, and storing the attributes which dynamically change along with time in a preset container; performing function calculation respectively for a first updating strategy without time sequence and a second updating strategy with time sequence to obtain a first parameter and a second parameter for calculating the value of the attribute which dynamically changes along with time; and processing all attributes in the preset container in batch through a preset image processing algorithm, selecting particles in a survival state and a visible state to write into the vertex cache region, reading the particles in the vertex cache region through a shader, and calculating the attribute value of each particle through other constant parameters in the shader. By the flexible use of the shader and the classification processing of the updating strategy, the particle attribute rendering efficiency is effectively improved.

Description

Particle system rendering method, apparatus, electronic device and readable storage medium

technical field

The present invention relates to the field of data processing, and in particular, to a particle system rendering method, device, electronic device and readable storage medium.

Background technique

In reality, there are many phenomena that contain a large number of tiny particles with similar behaviors, such as fireworks, leaves, water, clouds, etc. In 3D games, particle systems are used to simulate these phenomena. Since the geometric characteristics of particles are relatively simple, quadrilaterals can be used. Render, and then achieve the desired effect by updating the position, pose, size and texture of the particle system in real time. However, when the particle size increases, the update of the particles will have strict requirements on the performance of the computer. If the calculation speed cannot keep up, the effect of real-time rendering will not be achieved.

It can be seen that the existing rendering schemes cannot meet the rendering needs of large-scale particles.

SUMMARY OF THE INVENTION

In order to solve the above technical problems, the embodiments of the present application provide a particle system rendering method, device, electronic device, and readable storage medium, and the specific solutions are as follows:

In a first aspect, an embodiment of the present application provides a particle system rendering method, where the particle system rendering method includes:

The attributes of each particle in the preset particle cluster are classified and stored, wherein the attributes that change dynamically with time are stored in a preset container, and the preset particle cluster includes the same particle and the update strategy of each particle, and the update strategy Including a first update strategy without time series and a second update strategy with time series;

Calculate the first parameter corresponding to the first preset function based on the execution start point and execution end point of the first update strategy;

Calculate the second parameter corresponding to the second preset function based on the time series of the second update strategy and the value corresponding to each time node in the time series;

Batch process all attributes in the preset container based on the preset image processing algorithm, and write the particles in the alive state and the visible state at the current time into the vertex buffer;

In the shader, calculate all attribute values of each particle in the vertex buffer according to the first parameter, the first preset function, the second parameter, the second preset function and the current time;

Attribute rendering is performed on all particles in the preset particle cluster according to all attribute values of each particle.

According to a specific implementation of the embodiment of the present application, the preset container is a SOA_VECTOR container, and the step of classifying and storing the attributes of each particle in the preset particle cluster includes:

dividing the attributes of each particle in the preset particle cluster into attributes that change with time and constant attributes;

Integrate the constant attributes of all particles in the preset particle cluster into a structure, and store the pointer of the structure in each particle object in the preset particle cluster;

Store all time-varying properties in the SOA_VECTOR container.

According to a specific implementation of the embodiment of the present application, the first preset function is a one-dimensional linear function, wherein the dependent variable is an attribute value, and the independent variable is time, and is calculated based on the execution start point and execution end point of the first update strategy The step corresponding to the first parameter of the first preset function includes:

If the execution start point and execution end point of the current first update strategy are at the same time, the first coefficient group is acquired according to the first rule, and the first coefficient group is used as the first coefficient group when the current first update strategy is adopted. a parameter;

If the time of the execution start point and the execution end point of the current first update strategy are different, a second coefficient group is obtained according to the second rule, and the second coefficient group is used as the first update strategy when the current first update strategy is adopted. a parameter.

According to a specific implementation of the embodiment of the present application, the second preset function is a polynomial multi-degree polynomial with the number of time nodes of the second update strategy, wherein the dependent variable is an attribute value, and the independent variable is time ;

The step of calculating the second parameter corresponding to the second preset function based on the time series of the second update strategy and the value corresponding to each time node in the time series includes:

The least squares method is used to fit the multi-order polynomial in one variable, so as to obtain a time matrix and an attribute value vector corresponding to the time series;

The second parameter is calculated according to a preset calculation formula A ^T Ax=A ^T B, where A is a time matrix, B is an attribute value vector, and x is the second parameter.

According to a specific implementation of the embodiment of the present application, before the step of performing attribute rendering on all particles in the preset particle cluster according to all attribute values of each particle, the method further includes:

Calculate the observation transformation matrix and the projection transformation matrix according to the actual scene parameters, where the actual scene parameters include camera parameters, observation point parameters and screen parameters;

The viewing transformation matrix and the projection transformation matrix are written into the shader, so that the shader performs particle rendering according to the viewing transformation matrix and the projection transformation matrix.

According to a specific implementation of the embodiment of the present application, after the step of performing attribute rendering on all particles in the preset particle cluster according to all attribute values of each particle, the method includes:

Replace the current time with the next update time of the current time;

Jump to the step of batch processing the attribute data in the preset container based on the preset image processing algorithm, and perform the subsequent update rendering step until the preset time is reached.

According to a specific implementation of the embodiment of the present application, the preset image processing algorithm is a SIMD algorithm, and the attribute data in the preset container includes particle survivability, visibility, and particle structure transformation. Based on the preset image The step of processing the attribute data in the preset container in batches by the processing algorithm includes:

Judging the state of the particle at the current time according to the particle's survivability, visibility and particle structure transformation;

Write the particles that are alive and visible at the current time to the vertex buffer;

Initialize the particles that are in the dead state at the current time and store them in the preset container.

In a second aspect, an embodiment of the present application provides a particle system rendering device, where the particle system rendering device includes:

The classification storage module is used to classify and store the attributes of each particle in the preset particle cluster, wherein the attributes that change dynamically with time are stored in the preset container, and the preset particle cluster includes the same particle and the update of each particle a strategy, the update strategy includes a first update strategy without a time series and a second update strategy with a time series;

a first calculation module, configured to calculate a first parameter corresponding to a first preset function based on an execution start point and an execution end point of the first update strategy, wherein the first update strategy is an update strategy without a time series;

The second calculation module is configured to calculate the second parameter corresponding to the second preset function based on the time series of the second update strategy and the value corresponding to each time node in the time series, wherein the second update The strategy is an update strategy with time series;

a processing module, configured to batch process all attributes in the preset container based on a preset image processing algorithm, and write the particles that are in the alive state and the visible state at the current time into the vertex buffer;

A third calculation module, configured to calculate, in the shader, each particle in the vertex buffer according to the first parameter, the first preset function, the second parameter, the second preset function and the current time All attribute values of ;

The rendering module is configured to perform attribute rendering on all particles in the preset particle cluster according to all attribute values of each particle.

In a third aspect, an embodiment of the present application provides an electronic device, the electronic device includes a processor and a memory, the memory stores a computer program, and the computer program executes the first aspect when running on the processor. The described particle system rendering method.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and the computer program executes the particle system described in the first aspect when running on a processor render method.

Embodiments of the present application provide a particle system rendering method, device, electronic device, and readable storage medium, where the particle system rendering method includes: classifying and storing attributes of each particle in a preset particle cluster, The changed attributes are stored in the preset container; the function calculation is respectively performed for the first update strategy without time series and the second update strategy with time series to obtain the first update strategy for calculating the value of the attribute that changes dynamically with time. parameter and the second parameter; batch process all the attributes in the preset container through the preset image processing algorithm, select the particles in the alive state and the visible state to write to the vertex buffer area, so that the vertex buffer can be read through the shader Particles in the area, and calculate the attribute values of each particle through other constant parameters in the shader. Through the flexible use of shaders and the classification of update strategies, the efficiency of particle attribute rendering is effectively improved.

Description of drawings

In order to illustrate the technical solutions of the present invention more clearly, the accompanying drawings required in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be It is regarded as the limitation of the protection scope of the present invention. In the various figures, similar components are numbered similarly.

FIG. 1 shows a schematic flowchart of a method for rendering a particle system provided by an embodiment of the present application;

2 shows a schematic diagram of the effect of SOA_VECTOR container storage data types in a particle system rendering method provided by an embodiment of the present application;

FIG. 3 shows a schematic diagram of device modules of a rendering device for a particle rendering system provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments.

The components of the embodiments of the invention generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present invention.

Hereinafter, the terms "comprising", "having" and their cognates, which may be used in various embodiments of the present invention, are only intended to denote particular features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the presence of or adding one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing or the possibility of a combination of the foregoing.

Furthermore, the terms "first", "second", "third", etc. are only used to differentiate the description and should not be construed as indicating or implying relative importance.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of this invention belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized or overly formal meaning, unless explicitly defined in the various embodiments of the present invention.

Referring to FIG. 1 , a schematic flowchart of a method for rendering a particle system provided by an embodiment of the present application is provided. In the method for rendering a particle system provided by an embodiment of the present application, as shown in FIG. 1 , the particle system rendering method includes:

Step S101, classify and store the attributes of each particle in the preset particle cluster, wherein the attributes that change dynamically with time are stored in a preset container, and the preset particle cluster includes the same type of particles and the update strategy of each particle, so The update strategy includes a first update strategy without time series and a second update strategy with time series;

In a specific embodiment, a particle object generally includes two types of attribute data. The first type of attribute data is fixed attribute data that has been set for the particle object by the user before the particle runs. The first type of attribute data does not change during particle operation. Particle objects with attribute data of the first type are often associated with a first update strategy that does not have time series.

The second type of attribute data is the attribute data that will change with time during the running of the particle object, such as the current position, zoom factor, texture coordinates, force range and speed, etc. The value of the second type of attribute data is determined by the update strategy and the time series. Particle objects with attribute data of the second type are often associated with a second update strategy with time series.

In the actual running process, each particle object will be cloned multiple times at different time points, and the new cloned particle object has exactly the same attributes and update strategy as the original particle object. However, since the new particle object and the original particle object appear at different time points, the attribute values during subsequent simulation calculations will also be different.

In this embodiment, a set of new particle objects corresponding to each particle object and its cloned particle objects is formed into a particle cluster, and the attribute data of the particle objects in the particle cluster is classified and divided, according to the attributes of the particle objects. The type of data is stored accordingly.

According to a specific implementation of the embodiment of the present application, the preset container is a SOA_VECTOR container, and the step of classifying and storing the attributes of each particle in the preset particle cluster includes:

dividing the attributes of each particle in the preset particle cluster into attributes that change with time and constant attributes;

Integrate the constant attributes of all particles in the preset particle cluster into a structure, and store the pointer of the structure in each particle object in the preset particle cluster;

Store all time-varying properties in the SOA_VECTOR container.

In a specific embodiment, the preset container adopts a SOA_VECTOR container, and an implementation method of the SOA_VECTOR container may be implemented by using an existing method, which is not repeated in this embodiment.

The SOA_VECTOR container is a storage container that stores the attributes in the particle object separately as an attribute array, which is different from the VECTOR container which stores the particle object as a whole.

As shown in Figure 2, when the attributes of the particle object include a, b, c, and d, when VECTOR stores the attribute data of the particle object, it stores the attributes according to the complete particle object. When the user calls attribute data from VECTOR, only a complete particle object can be called.

When the SOA_VECTOR container stores the particle objects, the attributes of each particle object are separately stored as an attribute array. As shown in Figure 2, the SOA_VECTOR container includes a attribute array, b attribute array, c attribute array and d attribute array. .

In this embodiment, the attribute data that changes with time is stored in SOA_VECTOR. Specifically, when creating a SOA_VECTOR container, the user can set the memory alignment number of a single attribute and attribute array, and the number of objects to be calculated together. When adding an object, SOA_VECTOR will store the attributes of the object in the corresponding memory areas, and ensure that the content addresses of all data meet the requirements. When accessing data, users can read a certain attribute of all objects individually through the pointer provided by SOA_VECTOR.

For example, the SOA_VECTOR container described in Figure 2 sets the storage addresses of 4 types of attributes. When the processor calls the attributes from the SOA_VECTOR container, the a attribute array of all objects can be read individually.

For constant attributes, the particle clusters in this embodiment store the same kind of particles and the update policies of each particle, and in the same particle cluster, the constant attributes stored among the particle objects are all the same. For the same particle cluster, all constant attributes are integrated into a structure, and a pointer to the structure is stored in each particle object.

Specifically, the structure is structured data composed of a batch of data, the constant data is integrated into a structure, and the storage method of storing the pointer in the particle object can save the storage space of the particle object to the greatest extent. .

Step S102, calculating a first parameter corresponding to the first preset function based on the execution start point and execution end point of the first update strategy;

In a specific embodiment, for the first update strategy that does not have a time series, this embodiment uses a first preset function to replace the first update strategy, and according to the execution start point and execution end point of the first update strategy A coefficient corresponding to the first preset function is calculated.

The first update strategy includes particle rotation, texture rotation, texture mirror and other update strategies that do not have time series.

The selection of the execution starting point and the execution end point can be adaptively set by the user according to the actual application scenario. For example, the user can select the function interval for executing the first update strategy by himself, and the execution starting point and the execution end point are the function interval. the left and right endpoints.

For example, the particle object rotates from 0 seconds, from 90 degrees to 180 degrees, and the speed is 30 degrees per second, then you can set the execution start point to (0, 90), and set the execution end point to (3, 180).

According to a specific implementation of the embodiment of the present application, the first preset function is a one-dimensional linear function, wherein the dependent variable is an attribute value, and the independent variable is time, and is calculated based on the execution start point and execution end point of the first update strategy The step corresponding to the first parameter of the first preset function includes:

If the execution start point and execution end point of the current first update strategy are at the same time, the first coefficient group is acquired according to the first rule, and the first coefficient group is used as the first coefficient group when the current first update strategy is adopted. a parameter;

If the time of the execution start point and the execution end point of the current first update strategy are different, a second coefficient group is obtained according to the second rule, and the second coefficient group is used as the first update strategy when the current first update strategy is adopted. a parameter.

In a specific embodiment, a univariate linear function aX+b=Y is used instead of the first update strategy, where X is time, Y is attribute value, and a and b are first parameters.

b＝Y1-aX1；(X1≠X2)计算所述第一参数。When the execution start point and execution end point of the first update strategy are at the same time, according to the preset calculation formula

b=Y1-aX1; (X1≠X2) calculate the first parameter.

When the time of the execution start point and the execution end point of the first update strategy are different, the first parameter is calculated according to the preset calculation formula a=0; b=Y1; (X1=X2).

Specifically, the execution start point and the execution end point are a function interval selected by yourself. For the update strategy part of the particle outside the function interval, the time remainder outside the interval can be mapped to the interval. calculate.

Step S103, calculating the second parameter corresponding to the second preset function based on the time series of the second update strategy and the value corresponding to each time node in the time series;

In a specific embodiment, the second update strategy includes a time series update strategy such as position, color, rotation, and zoom. Specifically, for the update strategy with time series, there are different time nodes. The function of the second update strategy is a piecewise function with a corresponding number of time nodes.

For the second update strategy, a multi-degree polynomial of one variable is used to replace the piecewise function formed by the time series. For example, for the second update strategy with three time nodes, this embodiment uses a cubic polynomial cX ³ +dX ² +eX+f=Y to replace the second update strategy, where X is time and Y is Attribute value, c, d, e, f are the second parameters.

After the multi-order polynomial corresponding to the second update strategy is obtained, the second parameter of the multi-order polynomial can be calculated according to the attribute value corresponding to each time node.

According to a specific implementation of the embodiment of the present application, the second preset function is a polynomial multi-degree polynomial with the number of time nodes of the second update strategy, wherein the dependent variable is an attribute value, and the independent variable is time ;

The step of calculating the second parameter corresponding to the second preset function based on the time series of the second update strategy and the value corresponding to each time node in the time series includes:

The least squares method is used to fit the multi-order polynomial in one variable, so as to obtain a time matrix and an attribute value vector corresponding to the time series;

The second parameter is calculated according to a preset calculation formula A ^T Ax=A ^T B, where A is a time matrix, B is an attribute value vector, and x is the second parameter.

In a specific embodiment, for the selection of the multi-degree polynomial in one variable, reference may be made to the foregoing implementation manner, and details are not described herein again.

B＝(Y1，Y2，···，Yn)，其中A矩阵为对应所述时间序列的时间矩阵，B向量为对应所述时间序列的属性值向量。After obtaining the time value and attribute value of each time node in the corresponding time series, the one-variable multi-degree polynomial can be fitted based on the least squares method to obtain the matrix and vector forms of the corresponding time series. By fitting the cubic polynomial based on the least squares method, the sorted time series can be written in the form of A matrix and B vector, where,

B=(Y1, Y2, ···, Yn), wherein the A matrix is a time matrix corresponding to the time series, and the B vector is an attribute value vector corresponding to the time series.

After obtaining the time matrix and the attribute value vector, calculate the second parameter x of the second update strategy according to the formula A ^T Ax=A ^T B, where the second parameter x is a multi-dimensional vector corresponding to the number of the second parameters . For example, it corresponds to the coefficients (c, d, e, f) of a cubic polynomial.

For the first parameter corresponding to the first update strategy and the second parameter corresponding to the second update strategy, the parameter value will not change during the particle update process. A parameter and the second parameter are written into constants of a shader of a graphics processing unit (GPU for short).

Specifically, both a central processing unit (central processing unit, CPU for short) and a graphics processing unit GPU can perform computing tasks, but the application scenarios are different. The CPU is oriented to general-purpose tasks, needs to deal with various data types, and supports a large number of branch jumps and interrupt processing brought by logical judgment, and the internal structure is complex.

The GPU is only oriented to graphics computing tasks, the data types to be processed are highly uniform and independent of each other, and there is no need to consider the interruption of computing, and the internal structure is simple. Therefore, the efficiency of GPU is much higher than that of CPU in specific computing tasks. Shader Shader refers to a program running on the GPU.

Step S104, batch process all attributes in the preset container based on a preset image processing algorithm, and write the particles in the alive state and the visible state at the current time into the vertex buffer;

In a specific embodiment, the preset image processing algorithm may use a single instruction multiple data stream (Single Instruction Multiple Data, SIMD for short) image processing algorithm.

Specifically, the SIMD image processing algorithm can calculate multiple data in the same time, thereby speeding up the calculation efficiency of the central processing unit for processing the attributes in the preset container.

Specifically, the attributes processed by the preset image processing method include the survivability, visibility, particle structure transformation and other attributes that cannot be put into the shader for calculation.

The preset image processing algorithm can call corresponding attribute values from the SOA_VECTOR container, and set the number of attributes that can be processed simultaneously in one operation, thereby obtaining transformed attribute values such as particle survivability and visibility.

According to a specific implementation of the embodiment of the present application, the preset image processing algorithm is a SIMD algorithm, and the attribute data in the preset container includes particle survivability, visibility, and particle structure transformation. Based on the preset image The step of processing the attribute data in the preset container in batches by the processing algorithm includes:

Judging the state of the particle at the current time according to the particle's survivability, visibility and particle structure transformation;

Write the particles that are alive and visible at the current time to the vertex buffer;

Initialize the particles that are in the dead state at the current time and store them in the preset container.

In a specific embodiment, after processing all the attributes that cannot be put into the shader in the SOA_VECTOR container, the central processing unit writes the particles in the alive state and the visible state at the current time into the vertex buffer area to For the graphics processor to call from the vertex buffer.

For the particles that are in the dead state at the current time, set each attribute value of the particle object as the initial value, and store it in the SOA_VECTOR container again for the central processor to call in the next update.

Step S105, in the shader, calculate all attribute values of each particle in the vertex buffer according to the first parameter, the first preset function, the second parameter, the second preset function and the current time ;

In a specific embodiment, the first preset function is aX+b=Y, and the second preset function is cX ³ +dX ² +eX+f=Y. The first parameter (a, b) and the second parameter (c, d, e, f) are stored in the shader.

In the processing process of the above embodiment, the central processing unit processes the changing attribute values of each particle according to the preset image processing algorithm, and stores the particles in the alive state and the visible state in the vertex buffer area.

In the shader of the GPU, all relevant attribute values of all particles stored in the vertex buffer area can be calculated according to the first parameter, the first preset function, the second parameter and the second preset function, Property values such as position, color, rotation, scale, texture coordinates, etc.

According to a specific implementation of the embodiment of the present application, before the step of performing attribute rendering on all particles in the preset particle cluster according to all attribute values of each particle, the method further includes:

Calculate the observation transformation matrix and the projection transformation matrix according to the actual scene parameters, where the actual scene parameters include camera parameters, observation point parameters and screen parameters;

The viewing transformation matrix and the projection transformation matrix are written into the shader, so that the shader performs particle rendering according to the viewing transformation matrix and the projection transformation matrix.

In a specific embodiment, before rendering each particle object, the observation transformation matrix and the projection transformation matrix also need to be calculated according to the actual scene for rendering. Specifically, the calculation methods of the observation transformation matrix and the projection transformation matrix may adopt the calculation methods in the prior art, which will not be repeated here.

Specifically, the actual scene parameters include camera parameters, observation point parameters, screen width and height parameters, and the like.

The observation transformation matrix and the projection transformation matrix are input into the shader as constants, so that the shader can perform real-time rendering of each particle according to actual scene rendering requirements.

Step S106: Perform attribute rendering on all particles in the preset particle cluster according to all attribute values of each particle.

In a specific embodiment, after calculating all the attribute values of each particle in the vertex buffer by the shader, the particle can be determined according to the observation transformation matrix, the projection transformation matrix and all the attribute values of each particle calculated in the above-mentioned embodiment. All particles in the cluster are rendered.

Specifically, after using the shader to complete the above-mentioned attribute calculation step, the step of rendering may use a conventional particle rendering step, which will not be repeated here.

According to a specific implementation of the embodiment of the present application, after the step of performing attribute rendering on all particles in the preset particle cluster according to all attribute values of each particle, the method includes:

Replace the current time with the next update time of the current time;

Jump to the step of batch processing the attribute data in the preset container based on the preset image processing algorithm, and perform the subsequent update rendering step until the preset time is reached.

In a specific embodiment, the time point for rendering is continuously updated, and when the step of rendering all particles at the current time point is completed, the current time is replaced with the time of the next update. Specifically, the time for the next update may be set according to an actual application scenario, which is not specifically limited in this embodiment.

After replacing the current time, jump to the step of calculating the attribute data in the preset container in the above embodiment, so as to detect the survival and visibility of the particles at the time point of the next update, and according to the survival and visibility of the particles Do the next rendering calculation.

In the above embodiment, steps S101 to S104 are all performed in a central processing unit, and steps S105 and S106 are performed in a graphics processor of a graphics processor.

The particle system rendering method provided by this embodiment converts the calculation of particle attributes into functions that can be calculated in the shader and parameters corresponding to the functions by classifying and replacing the calculation of particle attributes, thereby reducing the memory of the central processing unit. The space occupied, and the frequent address changes in the central processing unit are reduced and multiple instructions are executed at the same time, which improves the utilization rate of the central processing unit. Some attribute updates of particles are calculated by the graphics processor, which improves the rendering efficiency of particle effects.

Referring to FIG. 3 , which is a schematic diagram of a device module of a particle system rendering apparatus 300 provided by an embodiment of the present application, the particle system rendering apparatus 300 provided by an embodiment of the present application, as shown in FIG. 1 , the particle system rendering apparatus 300 includes:

The classification storage module 301 is used to classify and store the attributes of each particle in the preset particle cluster, wherein the attributes that change dynamically with time are stored in a preset container, and the preset particle cluster includes the same particle and the attributes of each particle. an update strategy, the update strategy includes a first update strategy without a time series and a second update strategy with a time series;

a first calculation module 302, configured to calculate a first parameter corresponding to a first preset function based on an execution start point and an execution end point of the first update strategy, wherein the first update strategy is an update strategy without a time series;

The second calculation module 303 is configured to calculate the second parameter corresponding to the second preset function based on the time series of the second update strategy and the value corresponding to each time node in the time series, wherein the second The update strategy is an update strategy with time series;

The processing module 304 is configured to batch process all attributes in the preset container based on a preset image processing algorithm, and write the particles in the alive state and the visible state at the current time into the vertex buffer;

The third calculation module 305 is configured to, in the shader, calculate each element in the vertex buffer according to the first parameter, the first preset function, the second parameter, the second preset function and the current time. All attribute values of the particle;

The rendering module 306 is configured to perform attribute rendering for all particles in the preset particle cluster according to all attribute values of each particle.

In addition, an embodiment of the present application further provides an electronic device, the electronic device includes a processor and a memory, the memory stores a computer program, and the computer program executes the above-mentioned embodiments when running on the processor. Particle system rendering method.

An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and the computer program executes the particle system rendering method in the foregoing embodiment when running on a processor.

To sum up, the embodiments of the present application provide a particle system rendering method, device, electronic device, and storage medium. The particle system rendering method provided by the embodiments of the present application converts some attributes of particles and update strategies into functions and The method of parameters is stored in the shader to calculate the attribute values of some attributes through the shader, so as to continue to complete the rendering in the GPU, reducing the frequent address changes in the CPU and executing multiple instructions at the same time, improving the CPU utilization is reduced and memory space usage is reduced. Improved rendering efficiency of particle effects by using the GPU to compute partial particle updates. In addition, for the specific implementation process of the particle system rendering apparatus, the electronic device, and the computer-readable storage medium mentioned in the foregoing embodiments, reference may be made to the specific implementation process of the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may also be implemented in other manners. The apparatus embodiments described above are only schematic, for example, the flowcharts and structural diagrams in the accompanying drawings show the possible implementation architectures and functions of the apparatuses, methods and computer program products according to various embodiments of the present invention and operation. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more functions for implementing the specified logical function(s) executable instructions. It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart diagrams, can be implemented using dedicated hardware-based systems that perform the specified functions or actions. be implemented, or may be implemented in a combination of special purpose hardware and computer instructions.

In addition, each functional module or unit in each embodiment of the present invention may be integrated to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

If the functions are implemented in the form of software function modules and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: U disk, removable hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention. should be included within the protection scope of the present invention.

Claims (10)

1. A particle system rendering method, comprising:

classifying and storing the attributes of each particle in a preset particle cluster, wherein the attributes which dynamically change along with time are stored in a preset container, the preset particle cluster comprises the same type of particles and the updating strategies of each particle, and the updating strategies comprise a first updating strategy without a time sequence and a second updating strategy with the time sequence;

calculating a first parameter corresponding to a first preset function based on the execution starting point and the execution end point of the first updating strategy;

calculating a second parameter corresponding to a second preset function based on the time series of the second updating strategy and the numerical value corresponding to each time node in the time series;

processing all attributes in the preset container in batches based on a preset image processing algorithm, and writing the particles in a survival state and a visible state at the current time into a vertex cache region;

in a shader, calculating all attribute values of each particle in the vertex cache region according to the first parameter, the first preset function, the second parameter, the second preset function and the current time;

and respectively performing attribute rendering on all the particles in the preset particle cluster according to all the attribute values of all the particles.

2. The particle system rendering method according to claim 1, wherein the preset container is an SOA _ VECTOR container, and the step of storing the attributes of the particles in the preset particle cluster in a classified manner includes:

dividing the attribute of each particle in the preset particle cluster into an attribute which changes along with time and a constant attribute;

integrating the constant attributes of all the particles in the preset particle cluster into a structural body, and storing a pointer of the structural body in each particle object in the preset particle cluster;

storing all time-varying attributes in the SOA _ VECTOR container.

3. The particle system rendering method according to claim 1, wherein the first preset function is a unary linear function, wherein the dependent variable is an attribute value, the independent variable is a time, and the step of calculating the first parameter corresponding to the first preset function based on the execution start point and the execution end point of the first update strategy comprises:

if the time of the execution starting point and the execution ending point of the current first updating strategy is the same, acquiring a first coefficient group according to a first rule, and taking the first coefficient group as a first parameter when the current first updating strategy is adopted;

and if the execution starting point and the execution ending point of the current first updating strategy are different in time, acquiring a second coefficient group according to a second rule, and taking the second coefficient group as a first parameter when the current first updating strategy is adopted.

4. The particle system rendering method of claim 1, wherein the second predetermined function is a univariate polynomial of the number of time nodes of the second update strategy, wherein a dependent variable is an attribute value and an independent variable is time;

calculating a second parameter corresponding to a second preset function based on the time series of the second update strategy and the numerical value corresponding to each time node in the time series, wherein the step comprises the following steps:

fitting the unary multiple polynomial by adopting a least square method to obtain a time matrix and an attribute value vector corresponding to the time sequence;

according to a preset calculation formula A^TAx＝A^TAnd B, calculating the second parameter, wherein A is a time matrix, B is an attribute value vector, and x is the second parameter.

5. The particle system rendering method according to claim 1, wherein before the step of performing the attribute rendering on all the particles in the preset particle cluster according to all the attribute values of the particles, the method further comprises:

calculating an observation transformation matrix and a projection transformation matrix according to actual scene parameters, wherein the actual scene parameters comprise camera parameters, observation point parameters and screen parameters;

and writing the observation transformation matrix and the projection transformation matrix into the shader so that the shader performs particle rendering according to the observation transformation matrix and the projection transformation matrix.

6. The particle system rendering method according to claim 1, wherein after the step of performing attribute rendering on all particles in the preset particle cluster according to all attribute values of each particle, the method comprises:

replacing the current time with a next update time of the current time;

skipping to the step of processing the attribute data in the preset container in batch based on the preset image processing algorithm, and executing the subsequent updating and rendering step until the preset moment is reached.

7. The particle system rendering method according to claim 1, wherein the preset image processing algorithm is a SIMD algorithm, the attribute data in the preset container includes survivability, visibility, and particle structure transformation of the particles, and the step of batch processing the attribute data in the preset container based on the preset image processing algorithm includes:

judging the state of the particle at the current time according to the survivability, the visibility and the particle structure transformation of the particle;

writing the particles in the survival state and the visible state in the current time into a vertex cache region;

initializing the particles in the death state at the current time, and storing the particles in a preset container.

8. A particle system rendering apparatus, characterized in that the particle system rendering apparatus comprises:

the classified storage module is used for classified storage of attributes of each particle in a preset particle cluster, wherein the attributes which dynamically change along with time are stored in a preset container, the preset particle cluster comprises the same type of particles and update strategies of each particle, and the update strategies comprise a first update strategy without a time sequence and a second update strategy with the time sequence;

a first calculating module, configured to calculate a first parameter corresponding to a first preset function based on an execution start point and an execution end point of the first update policy, where the first update policy is an update policy without a time sequence;

the second calculation module is used for calculating a second parameter corresponding to a second preset function based on the time sequence of the second updating strategy and the numerical value corresponding to each time node in the time sequence, wherein the second updating strategy is an updating strategy with the time sequence;

the processing module is used for processing all attributes in the preset container in batches based on a preset image processing algorithm and writing the particles in a survival state and a visible state at the current time into a vertex cache region;

a third calculating module, configured to calculate, in a shader, all attribute values of each particle in the vertex cache region according to the first parameter, the first preset function, the second parameter, the second preset function, and the current time;

and the rendering module is used for respectively performing attribute rendering on all the particles in the preset particle cluster according to all the attribute values of all the particles.

9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the memory storing a computer program which, when run on the processor, performs the particle system rendering method of any of claims 1 to 7.

10. A computer-readable storage medium, in which a computer program is stored which, when run on a processor, performs the particle system rendering method of any one of claims 1 to 7.

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