WO2020238136A1

WO2020238136A1 - Polygon space division-based photon mapping optimization method, device and system

Info

Publication number: WO2020238136A1
Application number: PCT/CN2019/123404
Authority: WO
Inventors: 王璐; 王欣; 康春萌; 徐延宁; 孟祥旭; 马蕾蕾
Original assignee: 山东大学
Priority date: 2019-05-29
Filing date: 2019-12-05
Publication date: 2020-12-03
Also published as: CN110211197A; CN110211197B

Abstract

A polygon space division-based photon mapping optimization method, a device and a system. Said method comprises: a Master node receiving a scene image, performing scene division to obtain geometric boundary information, inserting a virtual portal at a division line, and sending same to each Slave node (S1); each Slave node reading a sub-area of a scene (S2); for each Slave node, photons being emitted from a light source inside said node, and being stored in a data structure when reaching the virtual portal at the boundary of the area of said node, and the photons being transmitted to a corresponding node of an adjacent area when the number of photons reaches a threshold (S3); each Slave node performing coloring calculation on the sub-area of the scene, and sending coloring calculation information to the Master node (S4); and the Master node selecting, according to the received coloring calculation information of each Slave node, the light brightness of an intersection point closest to the light source as the global light brightness of the colored point (S5).

Description

Photon mapping optimization method, device and system based on polygonal space division

Technical field

The present disclosure belongs to the technical field of graphics realistic rendering, and relates to a photon mapping optimization method, device and system based on polygonal space division.

Background technique

The statements in this section merely provide background information related to the present disclosure, and do not necessarily constitute prior art.

In the fields of architecture, art, and especially digital cinema, the demand for the scale of drawing scenes and the demand for realism are increasing. In order to generate photo-realistic images, global illumination needs to be calculated to accurately simulate all lighting conditions in the scene. However, in global illumination calculations, the spatial scene that needs to be rendered may be very large, and the large-scale lighting calculation tasks and large-scale scene data storage requirements brought about by it pose new challenges for realistic rendering and visual special effects simulation. Therefore, how to shorten rendering time and reduce memory usage has become an increasingly important issue.

With the increasing development of distributed frameworks in recent years, more and more researchers combine distributed and rendering, and use the powerful computing power of clusters to reduce rendering time. In this way, not only the rendering time is greatly reduced, but the problem of memory load failure no longer exists. Aiming at the photon storage bottleneck of large-scale scenes, researchers have proposed a series of photon mapping internal and external memory scheduling methods, which play a certain role in the rendering of large-scale scenes. Most of these methods adopt special scene division strategies to suit the distribution characteristics of certain types of scene geometric data and reduce the amount of data exchange between internal and external storage.

However, during the research and development process, the inventor found that there is no efficient scene division method in the current method suitable for scenes of large exhibition halls.

Summary of the invention

In view of the deficiencies in the prior art, one or more embodiments of the present disclosure provide a photon mapping optimization method, device and system based on polygonal space division.

According to an aspect of one or more embodiments of the present disclosure, a photon mapping optimization method based on polygonal space division is provided.

A photon mapping optimization method based on polygon space division, the method includes:

The Master node receives the scene image, divides the scene, obtains the geometric boundary information, inserts a virtual portal at the dividing line, and sends it to each slave node respectively;

Each Slave node reads the sub-areas of the scene;

For each Slave node, photons are emitted from the light source inside the node, stored in the data structure when they reach the virtual portal of the node area boundary, and transmitted to the corresponding node in the adjacent area when the number of photons reaches the threshold;

Each Slave node performs the coloring calculation of the scene sub-region, and sends the coloring calculation information to the Master node;

The Master node selects the brightness of the intersection closest to the light source as the global brightness of the colored point according to the received coloring calculation information of each Slave node.

Further, in this method, the geometric scene information includes vertex information of a polygon, a unique identifier of the polygon, and a unique identifier of adjacent polygons of the polygon.

Further, in this method, the specific steps of the scene division by the Master node include:

Obtain the contour coordinate information of the scene according to the top view of the scene image;

According to the number of Slave nodes, divide the scene image into corresponding N parts;

Ensure that the sum of the segmentation lines of the scene is the shortest and the area difference of the sub-regions of each divided scene is the smallest.

Further, in the method, the specific step of dividing the scene image into corresponding N parts includes:

Assuming that the outer contour polygon of the scene is divided into N parts, the area of the target sub-polygon is calculated according to the area of the outer contour polygon;

The outer contour polygon of the scene is divided into two, and one area is the target subpolygon area; iteratively use the same method to divide the other subpolygon until the area of all the subpolygons divided is close to the target subpolygon area;

Determine the shortest scene dividing line.

Further, in this method, the specific steps of each slave node reading the sub-region of the scene include:

Distributed loading of objects in the sub-region of the scene is adopted, and the SAH strategy is used to construct the scene k-d tree, and the photon graph belonging to the region is generated inside the node.

Further, in this method, each Slave node performs photon tracking and intersection detection in its own node.

Further, in this method, each Slave node performs the coloring calculation of the scene subregion, and near the scene division line, an overlapped kd tree is used to store photons, light is emitted from the screen space, and the coloring point where the light intersects the geometry is recorded Calculate the radiance of the colored point by collecting photons through the photon graph, and complete the coloring calculation. The specific steps include:

Set the photon collection search radius;

When the distance between the colored point and the virtual portal is greater than or equal to the search radius, the brightness of the colored point is the radiance calculated from the photon graph inside the node;

When the distance between the shaded point and the virtual portal is less than the search radius, visit the photons in the adjacent segmented area, use the overlapped kd tree to organize the photon graph, and store the photons at the boundary of the segmented area repeatedly to the nodes in the adjacent area. The brightness of the colored point is Detect the radiance calculated from the photon graph in the photon graph inside the node.

According to an aspect of one or more embodiments of the present disclosure, a photon mapping optimization system based on polygonal space division is provided.

A photon mapping optimization system based on polygonal space division, the system includes: a Master node and several Slave nodes connected to it;

Each Slave node reads the sub-areas of the scene;

For each Slave node, the photon is emitted from the light source inside the node, and stored in the data structure when it reaches the virtual portal at the boundary of the node area, and the photon is transmitted to the corresponding node in the adjacent area when the number of photons reaches the threshold;

A photon mapping optimization method based on polygon space division, implemented in the Master node, the method includes:

Determine the shortest scene dividing line.

According to an aspect of one or more embodiments of the present disclosure, a computer-readable storage medium is provided.

A computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor of an electronic device and executing the method for optimizing photon mapping based on polygonal space division.

According to an aspect of one or more embodiments of the present disclosure, an electronic device is provided.

An electronic device including a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store a plurality of instructions, the instruction is suitable for being loaded by the processor and executed A photon mapping optimization method based on polygonal space division.

A photon mapping optimization method based on polygonal space division, implemented in the Slave node, the method includes:

The Slave node reads the sub-areas of the scene;

Each Slave node performs the coloring calculation of the sub-region of the scene, and sends the coloring calculation information to the Master node.

Further, in this method, the specific steps of the Slave node reading the sub-areas of the scene include:

Set the photon collection search radius;

An electronic device comprising a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store a plurality of instructions, the instructions are suitable for being loaded by the processor and executing the one A photon mapping optimization method based on polygonal space division.

The beneficial effects of the present disclosure:

The present disclosure provides a method, device and system for optimizing photon mapping based on polygonal space division, which has no obvious deviation in user vision; multiple nodes emit photons in parallel, and when the number of nodes increases, the photon emission time becomes shorter and shorter. The method has good scalability in the drawing stage. In the case of the same number of nodes, the photon emission time used by the division method proposed in this disclosure is shorter than the photon emission time based on the Kd tree division method. In the photon emission stage, the number of photons hitting the virtual portal is greater than that of the kd tree division method. The quantity should be small to reduce the transmission cost. The multiple nodes of the present disclosure successfully share the increased complexity of each scene, and the distributed architecture is suitable for drawing scenes with complex outer contours.

Description of the drawings

The drawings of the specification forming a part of the application are used to provide a further understanding of the application, and the exemplary embodiments and descriptions of the application are used to explain the application, and do not constitute an improper limitation of the application.

Fig. 1 is a flowchart of a method for optimizing photon mapping based on polygonal space division according to one or more embodiments;

Fig. 2(a) is a graph of potential segmentation edges c1 of polygon ABCD according to one or more embodiments;

Fig. 2(b) is a graph of potential segmentation edges c2 of polygon ABCD according to one or more embodiments;

Fig. 3 is a segmentation diagram with AD and BC as paired edges according to one or more embodiments;

4 is a diagram of the shortest dividing line in the triangle AGB according to one or more embodiments;

FIG. 5 is a diagram of the shortest dividing line in a trapezoidal GDHB according to one or more embodiments;

Fig. 6 is a photon image of an adjacent area that needs to be collected when the point of impact is very close to the virtual portal (dashed line) according to one or more embodiments;

Fig. 7 is a diagram of photons sent to neighboring nodes when they hit an overlapped area according to one or more embodiments.

Detailed ways:

The following will clearly and completely describe the technical solutions in one or more embodiments of the present disclosure in conjunction with the accompanying drawings in one or more embodiments of the present disclosure. Obviously, the described embodiments are only part of the implementation of the present disclosure. Examples, not all examples. Based on one or more embodiments of the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work shall fall within the protection scope of the present disclosure.

It should be pointed out that the following detailed descriptions are all illustrative and are intended to provide further explanations for the application. Unless otherwise specified, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

It should be noted that the terms used here are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprising" and/or "including" are used in this specification, they indicate There are features, steps, operations, devices, components, and/or combinations thereof.

It should be noted that the flowcharts and block diagrams in the drawings illustrate the possible implementation architecture, functions, and operations of the method and system according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagram may represent a module, program segment, or part of code, and the module, program segment, or part of code may include one or more for implementing various embodiments. Executable instructions for the specified logic function. It should also be noted that, in some alternative implementations, the functions noted in the block may also occur in a different order than that noted in the drawings. For example, two blocks shown in succession may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the flowchart and/or block diagram, and the combination of the blocks in the flowchart and/or block diagram, can be implemented using a dedicated hardware-based system that performs the specified functions or operations Or it can be implemented using a combination of dedicated hardware and computer instructions.

In the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other, and the present disclosure will be further described below with reference to the drawings and embodiments.

This embodiment proposes a distributed photon mapping algorithm for large indoor exhibition hall-like large scenes similar to museums. Because the outline of this type of scene is generally more complicated, there is no obvious room boundary indoors. And most of the exhibits are evenly placed inside the scene. Such large buildings are generally composed of tens of thousands of polygons and thousands of light sources. Therefore, if the memory cannot be loaded, it is necessary to divide the building into multiple units in a certain division method and load them in a distributed manner on each node. Each node only loads one of the units, so there is no need to worry about the memory load.

This embodiment proposes a scene division method based on the outline of the scene. This division method ensures that the divided sub-scene areas are roughly the same, and the sum of the dividing lines is the shortest. Insert a virtual portal at the dividing line to effectively organize the data transmission in the photon mapping process and maintain the connection between the dividing units.

Among them, the equal area ensures that the calculation amount of the intersection of each sub-area is approximately equal, so that the light intersection task of each node is more balanced, and the mutual waiting problem caused by unbalanced tasks during node communication is reduced, and the communication time is reduced; In addition, the total length of the dividing line that divides the scene is the shortest. When the height of the portal is the same, the sum of the surface area of the portal is the smallest compared to other division methods. So as to ensure that the data transmission between each sub-area is minimal. After the scene is divided, the master dispatches each divided subunit to the corresponding slave node, and each slave node processes one of the subunits separately, reducing the overall scene loading time.

Example one

Scene division and loading stage:

The master node divides the outline of the scene into N parts according to the division method proposed in this article, and sends the divided geometric boundary information to each slave node, and each slave node reads the sub-region of the scene.

S1: The Master node receives the scene image, divides the scene, obtains the geometric boundary information, inserts a virtual portal at the dividing line, and sends it to each slave node respectively;

S2: Each Slave node reads the sub-areas of the scene;

Photon emission stage:

S3: For each slave node, the photon is emitted from the light source inside the node, and stored in the data structure when it reaches the virtual portal at the boundary of the node area, and the photon is transmitted to the corresponding node in the adjacent area when the number of photons reaches the threshold;

For each slave node, photons are emitted from the light source inside the node, and photon tracking and intersection detection are performed in the node, just like photon emission from a single node. But when the photon reaches the virtual portal at the boundary of the node area, it will not be reflected or absorbed, but will be stored in a specific data structure. This data structure is responsible for storing all photons that hit this virtual portal. When the number of photons in this data structure reaches a certain value, the photons in the data structure are transmitted to the corresponding nodes in the adjacent area.

Drawing stage:

S4: Each Slave node performs the coloring calculation of the scene sub-region, and sends the coloring calculation information to the Master node;

S5: The Master node selects the brightness of the intersection closest to the light source as the global brightness of the colored point according to the received coloring calculation information of each Slave node.

In the drawing phase, the master node is responsible for sending eye rays to all slave nodes, calculating the intersection of light and scene on each slave node, and collecting photons at the intersection, calculating the brightness value of the point, and then each slave node The intersection position and the brightness value of the intersection are returned to the master node. The master node selects the intersection closest to the viewpoint and the corresponding brightness value as the brightness value of a sampling point until all eye rays calculations are completed.

In step S1 of this embodiment, the geometric scene information includes vertex information of a polygon, a unique identifier of the polygon, and a unique identifier of adjacent polygons of the polygon.

In step S1 of this embodiment, the specific steps of scene division by the Master node include:

In step S1 of this embodiment, the specific step of dividing the scene image into corresponding N parts includes:

Determine the shortest scene dividing line.

In step S2 of this embodiment, the specific steps for each slave node to read the sub-areas of the scene include:

Distributed loading of objects in the sub-region of the scene is adopted, and the SAH strategy is used to construct the scene k-d tree, and a photon graph belonging to the region is generated inside the node.

In the scene division and loading stage, the master node divides the scene outline into N parts according to the above division method, and sends the divided geometric boundary information to each slave node. The divided polygon needs to record the following information: the vertex information of the polygon. This information is recorded to determine whether an object in the scene belongs to the polygon when the object is loaded on the slave node. The unique identifiers of the adjacent polygons of the polygon should also be recorded, because only the slave nodes corresponding to the adjacent areas can communicate. In addition, the unique identification of the polygon also needs to be recorded.

The scene division and loading part are the following steps

Step (1.1): Find the matching side

For the outer contour polygon of a given scene, suppose the polygon needs to be divided into N parts. This article uses the divide-and-conquer algorithm: calculate the area of the outer contour polygon as A _poly , therefore, the area of each target sub-polygon A _tar to be divided is:

For the initial polygon, first divide this polygon into two, the area of the smaller one A _small is:

The area A _big of the larger polygon is:

Iteratively use the same method to divide the larger polygon until the area of the divided polygon meets the requirements of formula (1).

Step (1.2): Determine the dividing line

The dividing line must start on one side of the polygon and end on the other side of the polygon. For example, there are 6 possible combinations of edges and edges in a 4-sided polygon. So there may be a potential cut. As shown in Figure 1, there are two potential split edges c1, c2. For all potential split edges, this article chooses the shortest one.

Without loss of generality, take the quadrilateral in Figure 2(a) and Figure 2(b) as an example, choose AD and BC as the edge where the vertices of the dividing line are located. The first step is to determine the angle bisector formed by edge AD and edge BC , As shown in Figure 2 (a) and Figure 2 (b) dotted line, marked as m. In the second step, the four vertices are respectively projected on the opposite side along the direction perpendicular to the angle bisector m. If the projection point does not fall on the line segment of the opposite side, the projection point is ignored. For example, in Figure 3, the projection vertex of B is G, and the projection point of D is H. Project A along the direction perpendicular to the angle bisector m, and find that its projection point is not on the side BC, so vertex A is ignored. The same argument applies to point C. The effective projection points of the four vertices are G and H. The third step is to connect vertices B and D to their corresponding effective projection points G and H, forming a combination of triangles AGB, DCH and trapezoid GDHB. In the fourth step, if there is a minimum dividing line on side AD and side BC, they must be located in these two triangles or trapezoids. To find the position of the smallest dividing line, a simple area check can be performed. For example, A _AGB is the area of the left triangle, A _GDHB is the area of the trapezoid, and A _DCH is the area of the right triangle. There are three situations where the smallest dividing line is located:

In the first case, if

Then the minimum dividing line is in the triangle, as shown in Figure 4. One end of the dividing line coincides with point B, and the other end of the dividing line can be obtained by linear interpolation based on points A and G in the target area. The area of the target area is

The other end of the dividing line can be obtained by linear interpolation by formula (4):

In the second case, if

And

Then the minimum dividing line is in the trapezoid, as shown in Figure 5. The two vertices of the dividing line can be moved according to the size of the target area. If the dividing line needs to be the shortest, then the angle of the dividing line must always be perpendicular to the angle bisector m of side AD and side BC. One end of the dividing line is the linear interpolation of G and D. The other end of the dividing line is the linear interpolation of B and H.

The third case, if

Then the smallest dividing line is in the right triangle.

When the dividing line is in a trapezoid, the dividing line must be perpendicular to the angle bisector m of side AD and side BC. Geometric verification can be done by creating a trapezoid, where the parallel side and the angle bisector form an angle α. The area of this trapezoid can be expressed as a function of α. In order to make the dividing line the shortest, this article takes the derivative of the area, and it can be proved that for a given area, α must be equal to 90 degrees.

After the scene is divided, each slave node starts to read the geometric information and light source information of the divided sub-scene. Because the scene file is generally large, it can even reach dozens of G for some scene files. At this time, the memory cannot be loaded at one time. open. Therefore, a distributed method of loading objects in xml is adopted. When each slave node loads related objects in the scene, it uses SAH strategy to start building the scene kd tree. Since each slave node also loads the light source information, the photon graph belonging to the area can be generated inside the node. . Since each node does not need to store the global photons, it only needs to store the local photons, so the memory pressure of photon storage is reduced.

In step S3 of this embodiment, each Slave node performs photon tracking and intersection detection in its own node.

The photons in the photon graph of a node include the photons emitted from the area and the photons transmitted from the neighboring nodes through the virtual portal. The photon travels in the scene, and when the photon hits the diffuse surface, it will be stored in the photon map. Since each node loads one of the sub-regions, each node has a photon map belonging to the region. The photon map only needs to store the photons of the scene loaded by the node, and does not need to store the photons in the entire scene Therefore, the memory pressure is greatly reduced.

The photon emission stage is specifically as follows. Since each node is loaded with a light source within the range of each node, at each node, photons are emitted from the light source of this node, and intersection detection is performed in this node. Since each node only loads a sub-region of the scene, when performing intersection detection, this article only needs to consider the geometry in the sub-region. Therefore, the efficiency of intersection detection will be greatly improved.

When the photon travels in the scene, the photon may hit different surfaces. When the photon hits the surface of these objects in the scene, the Russian wheel is used to determine whether it will be reflected, continue to propagate or be absorbed. However, when the photon reaches the inserted virtual portal, it will not be reflected or absorbed, but will be stored in a specific data structure. This data structure is responsible for storing all photons that hit this virtual portal. The size of the array storing these photons is given in this article a fixed value MAX_TEMP_PHOTONS. When the number of photons in this data structure reaches this fixed value, the adjacent area is activated and the photons in the data structure are transmitted to the node corresponding to the adjacent area. in. When photons are transmitted to nodes in adjacent areas, these photons begin to intersect objects in the adjacent area, just like photons emitted from a light source. When the node has no computing tasks, this article will detect the number of photons on the virtual portal again at this time. At this time, even if the number of photons on the virtual portal does not reach MAX_TEMP_PHOTONS, all the photons on the virtual portal will be transmitted to the adjacent area, reducing the photon loss and improving the accuracy.

The photon travels in the scene. When the photon hits the diffuse surface, the photon will be stored in the relevant data structure. Once the photons are transmitted in the sub-region of the scene, the stored photons in the sub-region will be organized into a kd tree to build a photon graph. Since each node loads one of the sub-regions, each node has It belongs to the photon graph within this area. The photon graph only needs to store the photons in the scene loaded by this node, and does not need to store the photons in the entire scene. Therefore, the level of each kd tree will be greatly reduced. When using k nearest neighbors When the algorithm calculates the brightness of the colored points within the search radius, it will become more efficient, and this greatly reduces the memory pressure.

In step S4 of this embodiment, each Slave node performs the coloring calculation of the scene sub-region. Near the scene dividing line, an overlapped kd tree is used to store photons, light is emitted from the screen space, and the coloring of the intersection of light and geometry is recorded To calculate the radiance of the colored point by collecting photons through the photon graph, and complete the coloring calculation, the specific steps include:

Set the photon collection search radius;

When the distance between the shading point and the virtual portal is greater than or equal to the search radius, the brightness of the shading point is the radiance calculated from the photon graph inside the node;

The present invention uses an overlapped k-d tree to store photons to solve the problem of collecting photons at the coloring point and cross-border point at the boundary, and adopts an overlapped k-d tree. Set a range in the scene dividing line area. This article sets this range as the photon collection search radius, and this range is regarded as an overlapped area. If the photon after the photon emission falls within the boundary range, the photon needs to be stored in the adjacent area nodes. The specific method is to calculate the distance between the photon and the dividing plane in the x-axis, y-axis, and z-axis directions for each photon. If the distance between the photon and the x-axis, y-axis, and z-axis is less than the search radius , Then the photon needs to be sent to the adjacent area for further transmission in the adjacent area, just like the photon emitted from the light source. By repeatedly storing the photons at the boundary of the divided area to the nodes in the adjacent area, when calculating the brightness of a certain coloring point, only the photons in the photon graph inside the node need to be detected, and there is no communication cost, which improves the calculation efficiency.

In the photon collection stage, each node is only responsible for the coloring calculation of a sub-region of the scene. Finally, the coloring calculation information is sent to the master node, and the master node selects the brightness of the intersection closest to the light source as the global brightness of the coloring point. In the process of light estimation, light is emitted from the screen space, and the coloring point where the light intersects the geometry is recorded, and the radiance of the coloring point is calculated by photon graph collection, and the coloring calculation is completed. At the intersection point, use k-nearest neighbors to search the surrounding photons to estimate the brightness of the intersection point. At this time, the search radius needs to be determined. Given a search radius, for the intersection point far away from the virtual portal, the radiance calculated using the photon graph inside the node is the luminosity of the point. However, for the shading point at the boundary of the region within the node, that is, when the distance between the shading point and the virtual portal is less than the search radius, this will cause discontinuity of illumination. Therefore, it is necessary to access the photons of the adjacent divided regions. The present invention uses overlapped k-d tree to solve this problem, as shown in Figure 6. The specific method is to calculate the distance between the photon and the dividing plane in the x-axis, y-axis, and z-axis directions for each photon. If the distance between the photon and the x-axis, y-axis, and z-axis is less than the search radius , Then the photon needs to be sent to the adjacent area for further transmission in the adjacent area, just like the photon emitted from the light source. As shown in Figure 7, the overlapped area is between the dashed line and the dividing line. In order to use the k-nearest neighbor algorithm to quickly locate the photons within the radius of the intersection point between the light emitted by the screen space and the scene, this paper uses the k-d tree to organize the photon graph. By repeatedly storing the photons at the boundary of the divided area on the nodes in the adjacent area, when calculating the brightness of a coloring point, only the photons in the photon graph inside the node need to be detected, and there is no communication cost, which improves the calculation efficiency.

This embodiment can be extended to 128 nodes, and can be applied to both convex and concave outer contours, which fully solves the problem of rendering time and memory bottleneck in exhibition hall scenes. Not only that, the scene division algorithm of the present invention is compared with the kd tree-based division algorithm. Under the same number of nodes, the photon emission time used by the division method proposed in this paper is shorter than the photon emission time based on the kd tree method. Compared with the kd tree division method, the speed of this method is increased by more than 1.5 times.

Example two

A photon mapping optimization system based on polygonal space division, and the photon mapping optimization method based on polygonal space division described in the embodiment, the system includes: a Master node and several slave nodes connected to it;

Each Slave node reads the sub-areas of the scene;

Each Slave node performs the coloring calculation of the scene sub-area, and sends the coloring calculation information to the Master node;

Determine the shortest scene dividing line.

The Slave node reads the sub-areas of the scene;

Set the photon collection search radius;

Example two

Example three

When these computer-executable instructions run in the device, the device executes the methods or processes described in the various embodiments of the present disclosure.

In this embodiment, the computer program product may include a computer-readable storage medium, which carries computer-readable program instructions for executing various aspects of the present disclosure. The computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon The protruding structure in the hole card or the groove, and any suitable combination of the above. The computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .

The computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in one or more programming languages Source code or object code written in any combination of, the programming language includes object-oriented programming languages-such as C++, etc., and conventional procedural programming languages-such as "C" language or similar programming languages. Computer readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server carried out. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to access the Internet connection). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions. The computer-readable program instructions are executed to implement various aspects of the present disclosure.

The beneficial effects of the present disclosure are:

(1) With the increase of the number of nodes, the error range of the method in this paper is in a relatively small range, and there is no obvious deviation in the user's vision.

(2) During the photon emission stage, since multiple nodes emit photons in parallel, as the number of nodes increases, the photon emission time becomes shorter and shorter. Because multiple nodes emit photons in parallel to share the overall task load, even if it is affected by communication time, the time it takes to emit photons is still getting shorter and shorter. But from the perspective of parallel efficiency, whether 20 million photons, 36 million photons or 60 million photons are emitted, the parallel efficiency decreases with the increase in the number of nodes. The reason is that as the number of nodes increases, the communication overhead between nodes will gradually increase, resulting in a decrease in parallel efficiency. Even when the outer contour of the polygon is concave, the same rules apply. Therefore, no matter the outer contour is convex polygon or concave polygon, the algorithm in this paper can be effectively extended to 128 nodes, and the parallel efficiency is above 20%. Therefore, the algorithm in this paper can be applied to exhibition hall scenes whose outer contour is convex polygon, as well as exhibition hall scenes whose outer contour is concave polygon.

(3) In the drawing stage, this paper adopts a master-slave accelerated parallel mode with one master node and multiple slave nodes. The master node is responsible for emitting eye rays. These eye rays will be sent to each other slave node separately. On the slave node, eye ray will conduct an intersection test with each part of the scene loaded from the node, generate hit points, use the k-nearest neighbor algorithm to find the brightness of each hit point, and return the intersection point and the light estimation value of the intersection point For the master node, select the closest intersection point to the viewpoint on the master node, and use the light estimation value of the intersection point as the light estimation value of the pixel. Therefore, for the drawing stage, reducing the communication overhead between the master node and the slave node is the main problem. Using 4 nodes, emitting 250,000 photons, 8 sampling points per pixel, the time used in the drawing phase is 52.2405min, when the number of nodes is increased to 32, the time used in the drawing phase is 55.9685min, and the communication time is increased 3.728min, which shows that with the growth of nodes, the drawing phase does not consume too much communication time, so the method in this paper has good scalability in the drawing phase.

(4) In the case of the same number of nodes, the photon emission time used by the division method proposed in this paper is shorter than the photon emission time based on the Kd tree division method. When there are more than 8 nodes, this method is faster than the kd tree division method. Improved by more than 1.5 times. This shows that in the photon emission stage, the number of photons hitting the virtual portal is less than that based on the k-d tree division method, which reduces the transmission cost.

(5) Under different number of nodes, when the number of edges of the outline of the scene is increased from 8 edges to 100 edges, the photon emission time does not increase significantly. This is because although the complexity of the scene has increased, the number of objects in the scene and the length of the dividing line dividing the scene have not changed much, resulting in less obvious changes in the photon emission time. This shows that multiple nodes successfully share the increased complexity of each scene, and the distributed architecture is suitable for drawing scenes with complex outer contours.

The foregoing descriptions are only preferred embodiments of the application, and are not used to limit the application. For those skilled in the art, the application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc., made within the spirit and principle of this application shall be included in the protection scope of this application. Therefore, the present disclosure will not be limited to the embodiments shown in this document, but should conform to the widest scope consistent with the principles and novel features disclosed in this document.

Claims

A photon mapping optimization method based on polygon space division, characterized in that the method includes:

The Master node receives the scene image, divides the scene, obtains the geometric boundary information, inserts a virtual portal at the dividing line, and sends it to each slave node respectively;

Each Slave node reads the sub-areas of the scene;

For each Slave node, photons are emitted from the light source inside the node, stored in the data structure when they reach the virtual portal of the node area boundary, and transmitted to the corresponding node in the adjacent area when the number of photons reaches the threshold;

Each Slave node performs the coloring calculation of the scene sub-region, and sends the coloring calculation information to the Master node;

The Master node selects the brightness of the intersection closest to the light source as the global brightness of the colored point according to the received coloring calculation information of each Slave node.
A photon mapping optimization method based on polygon space division according to claim 1, wherein, in the method, the geometric scene information includes the vertex information of the polygon, the unique identifier of the polygon, and the information of the adjacent polygons of the polygon. Uniquely identifies.

And/or, in this method, the specific steps of scene division by the Master node include:

Obtain the contour coordinate information of the scene according to the top view of the scene image;

According to the number of Slave nodes, divide the scene image into corresponding N parts;

Ensure that the sum of the segmentation lines of the scene is the shortest and the area difference of the sub-regions of each divided scene is the smallest.

And/or, in the method, the specific step of dividing the scene image into corresponding N parts includes:

Assuming that the outer contour polygon of the scene is divided into N parts, the area of the target sub-polygon is calculated according to the area of the outer contour polygon;

The outer contour polygon of the scene is divided into two, and one area is the target subpolygon area; iteratively use the same method to divide the other subpolygon until the area of all the subpolygons divided is close to the target subpolygon area;

Determine the shortest scene dividing line.

And/or, in this method, the specific steps of each slave node reading the sub-region of the scene include:

Distributed loading of objects in the sub-region of the scene is adopted, and the SAH strategy is used to construct the scene k-d tree, and the photon graph belonging to the region is generated inside the node.

And/or, in this method, each Slave node performs photon tracking and intersection detection in its own node.

And/or, in this method, each Slave node performs the coloring calculation of the scene subregion, and uses an overlapped kd tree to store photons near the scene dividing line, emits light from the screen space, and records the coloring of the intersection of the light and the geometry Point, collect the photon through the photon graph to calculate the radiance of the colored point, and complete the coloring calculation, the specific steps include:

Set the photon collection search radius;

When the distance between the shading point and the virtual portal is greater than or equal to the search radius, the brightness of the shading point is the radiance calculated by the photon graph inside the node;

When the distance between the shaded point and the virtual portal is less than the search radius, visit the photons in the adjacent segmented area, use the overlapped kd tree to organize the photon graph, and store the photons at the boundary of the segmented area repeatedly to the nodes in the adjacent area. The brightness of the colored point is Detect the radiance calculated from the photon graph in the photon graph inside the node.
A photon mapping optimization system based on polygonal space division, characterized in that the system includes: a Master node and a number of Slave nodes connected to it;

The Master node receives the scene image, divides the scene, obtains the geometric boundary information, inserts a virtual portal at the dividing line, and sends it to each slave node respectively;

Each Slave node reads the sub-areas of the scene;

For each Slave node, photons are emitted from the light source inside the node, stored in the data structure when they reach the virtual portal of the node area boundary, and transmitted to the corresponding node in the adjacent area when the number of photons reaches the threshold;

Each Slave node performs the coloring calculation of the scene sub-region, and sends the coloring calculation information to the Master node;

The Master node selects the brightness of the intersection closest to the light source as the global brightness of the coloring point according to the received coloring calculation information of each Slave node.
A photon mapping optimization method based on polygonal space division, implemented in the Master node, is characterized in that the method includes:

The Master node receives the scene image, divides the scene, obtains the geometric boundary information, inserts a virtual portal at the dividing line, and sends it to each slave node respectively;

The Master node selects the brightness of the intersection closest to the light source as the global brightness of the coloring point according to the received coloring calculation information of each Slave node.
A photon mapping optimization method based on polygon space division according to claim 4, characterized in that, in the method, the geometric scene information includes the vertex information of the polygon, the unique identifier of the polygon, and the adjacent polygons of the polygon. Uniquely identifies.

And/or, in this method, the specific steps of scene division by the Master node include:

Obtain the contour coordinate information of the scene according to the top view of the scene image;

According to the number of slave nodes, divide the scene image into corresponding N parts;

Ensure that the sum of the segmentation lines of the scene is the shortest and the area difference of the sub-regions of each divided scene is the smallest.

And/or, in the method, the specific step of dividing the scene image into corresponding N parts includes:

Assuming that the outer contour polygon of the scene is divided into N parts, the area of the target sub-polygon is calculated according to the area of the outer contour polygon;

The outer contour polygon of the scene is divided into two, and one area is the target subpolygon area; iteratively use the same method to divide the other subpolygon until the area of all the subpolygons divided is close to the target subpolygon area;

Determine the shortest scene dividing line.
A photon mapping optimization method based on polygonal space division, implemented in the Slave node, characterized in that the method includes:

The Slave node reads the sub-areas of the scene;

For each Slave node, the photon is emitted from the light source inside the node, and stored in the data structure when it reaches the virtual portal at the boundary of the node area, and the photon is transmitted to the corresponding node in the adjacent area when the number of photons reaches the threshold;

Each Slave node performs the coloring calculation of the sub-region of the scene, and sends the coloring calculation information to the Master node.
A photon mapping optimization method based on polygonal space division according to claim 6, wherein, in the method, the specific steps of the Slave node reading the sub-regions of the scene include:

Distributed loading of objects in the sub-region of the scene is adopted, and the SAH strategy is used to construct the scene k-d tree, and a photon graph belonging to the region is generated inside the node.

And/or, in this method, each Slave node performs photon tracking and intersection detection in its own node.
A photon mapping optimization method based on polygonal space division according to claim 6, characterized in that, in the method, each slave node performs the coloring calculation of the scene sub-region, and near the scene division line, The overlapped kd tree stores photons, emits light from the screen space, records the shading point where the light intersects the geometry, collects the photon through the photon graph, calculates the radiance of the shading point, and completes the shading calculation. The specific steps include:

Set the photon collection search radius;

When the distance between the colored point and the virtual portal is greater than or equal to the search radius, the brightness of the colored point is the radiance calculated from the photon graph inside the node;

When the distance between the shaded point and the virtual portal is less than the search radius, visit the photons in the adjacent segmented area, use the overlapped kd tree to organize the photon graph, and store the photons at the boundary of the segmented area repeatedly to the nodes in the adjacent area. The brightness of the colored point is Detect the radiance calculated from the photon graph in the photon graph inside the node.
A computer-readable storage medium, wherein multiple instructions are stored, wherein the instructions are adapted to be loaded by a processor of an electronic device and executed as described in any one of claims 4-5 or 6-8 A photon mapping optimization method based on polygonal space division.
An electronic device comprising a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, characterized in that the instructions are suitable for being loaded and executed by the processor A photon mapping optimization method based on polygonal space division according to any one of claims 4-5 or 6-8.