WebGL-based cloud collaborative three-dimensional building modeling rendering method and system

Abstract

The invention provides a cloud collaborative three-dimensional building modeling rendering method and system based on WebGL (Web Graphics Library). The method comprises the following steps: acquiring design parameters of a target building, analyzing the design parameters into geometric topological data of a plurality of independent components on the basis of a functional attribute division rule, and generating a multi-stage detail geometric model; adding material physical attributes to the surface vertex data, calculating a diffuse reflection coefficient and specular reflection intensity through a ray tracing algorithm, and generating lightweight component data carrying physical rendering parameters; in response to a view angle switching instruction of a terminal user, according to a space block corresponding to a current view angle, a main GPU node is distributed from a distributed GPU cluster, the main GPU node is divided according to the space block, adjacent blocks of the space block are distributed to edge GPU nodes, parallel ray tracing calculation is executed based on a WebGL protocol, and the parallel ray tracing calculation is executed based on the WebGL protocol. And returning to the main GPU node synthesis terminal display image and displaying. According to the technical scheme provided by the invention, the response speed is remarkably improved while the physical-level rendering effect is kept.

Description

Technical Field

[0001] This application relates to the field of modeling and rendering technologies, and in particular, to a cloud-based collaborative 3D building modeling and rendering method and system based on WebGL. Background Art

[0002] In the scenarios of building information models and smart city management, complex building models need to display real-time lighting effects on various terminals (PCs, mobile devices), including the interaction between natural light and artificial light sources; need to dynamically adjust the model detail level according to the terminal performance and user viewing distance to balance the rendering quality and loading speed; need to support multiple users to modify model components simultaneously and synchronize the rendering results in real time to avoid data conflicts and delays.

[0003] The current mainstream solutions adopt a single-GPU rendering architecture based on the WebGL framework (such as Three.js, Babylon.js), and its process includes: loading building model data through JavaScript, directly calling the GPU through the WebGL interface for rasterization and shader calculation; using ray tracing algorithms to simulate lighting effects, but limited by the computing power of a single node, only supporting static models or dynamic scenes with low complexity.

[0004] However, a single GPU node cannot efficiently handle the parallel ray tracing calculations of large-scale building models, resulting in a sharp drop in the frame rate in complex lighting scenarios; lacking a distributed rendering mechanism, global re-rendering is required when multiple users edit, causing a sharp increase in network load and response delay. Summary of the Invention

[0005] This application provides a cloud-based collaborative 3D building modeling and rendering method and system based on WebGL to solve the problem of poor building modeling and rendering effects in the prior art.

[0006] In a first aspect, this application provides a cloud-based collaborative 3D building modeling and rendering method based on WebGL, including: Obtaining the design parameters of a target building from a cloud database, parsing the design parameters into geometric topology data of multiple independent components based on the functional attribute division rules of building components, and dynamically generating multi-level detail geometric models of each independent component according to the terminal display resolution and viewing distance; Attaching material physical properties to the surface vertex data of the multi-level detail geometric models, calculating the diffuse reflection coefficient and specular reflection intensity of the material physical properties and the building scene lighting conditions through ray tracing algorithms, and generating lightweight component data carrying physical rendering parameters; In response to the perspective switching instruction of the end user, according to the spatial block corresponding to the current perspective, a main GPU node is allocated from the distributed GPU cluster. The main GPU node divides the lightweight component data by spatial block, and allocates adjacent blocks of the spatial block to the edge GPU nodes. The edge GPU nodes perform parallel ray tracing calculations on the allocated blocks based on the WebGL protocol, return the calculation results to the main GPU node, synthesize the terminal display image and display it.

[0007] Optionally, it further includes: When the multi-user collaborative editing operation triggers a change in the geometric shape or material properties of at least one independent component, extract the three-dimensional spatial bounding box range of the changed component; Based on the vertex coordinate update value and material property change value within the three-dimensional spatial bounding box range, recalculate the diffuse reflection coefficient and specular reflection intensity of the changed component under the lighting direction of the building scene, and generate updated lightweight component data carrying physical rendering parameters; According to the corresponding spatial block of the three-dimensional spatial bounding box range, distribute the updated lightweight component data to the distributed GPU cluster, trigger the edge GPU nodes bound to the corresponding spatial block to perform parallel ray tracing secondary calculations, return the secondary calculation results to the main GPU node for local frame synthesis, and generate updated rendering blocks; Perform pixel-level difference comparison between the updated rendering block and the original display data in the unchanged area, extract the difference pixel coordinates and color values, and overwrite the original display data through the difference data stream.

[0008] Optionally, attaching the material physical properties to the surface vertex data of the multi-level detail geometric model, and calculating the diffuse reflection coefficient and specular reflection intensity of the material physical properties and the building scene lighting conditions through the ray tracing algorithm to generate lightweight component data carrying physical rendering parameters includes: Match the material type corresponding to the multi-level detail geometric model in the building material database, extract the surface roughness, refractive index and metallicity parameters of the material type, and map them to the surface vertex data of the multi-level detail geometric model; Based on the preset natural light direction and artificial light source position in the building scene, construct a light propagation path associated with the surface vertex data of the multi-level detail geometric model, and calculate the first reflection direction and energy attenuation value of each light path at the surface vertex coordinates of the multi-level detail geometric model according to the surface roughness and the refractive index; Dynamically adjusting the light sampling density of the surface vertex coordinates, wherein the number of secondary light sampling is increased for vertices in high curvature areas and the number of secondary light sampling is reduced for vertices in flat areas, to generate diffuse reflection coefficients and specular reflection intensity of the surface vertex coordinates; The diffuse reflection coefficient, the specular reflection intensity and the energy attenuation value are encapsulated as physical rendering parameters bound to the surface vertex coordinates, redundant vertex data not affected by illumination in the multi-level detail geometric model is eliminated, and lightweight component data carrying physical rendering parameters is generated.

[0009] Optionally, the step of constructing a light propagation path associated with the surface vertex data of the multi-level detail geometric model based on the preset natural light direction and artificial light source position in the architectural scene, and calculating the first reflection direction and energy attenuation value of each light path at the surface vertex coordinates according to the surface roughness and the refractive index includes: Reading the preset natural light direction angle and the three-dimensional space coordinates of the artificial light source from the building scene configuration file, and obtaining the radiation intensity and spectral distribution data of the artificial light source; Taking the surface vertex coordinates as the starting point, extending the light propagation path in the reverse direction along the natural light direction angle and the three-dimensional space coordinates, and establishing a direct illumination association relationship between the surface vertex coordinates and the artificial light source; Determine a microsurface normal perturbation range at the surface vertex coordinates according to the surface roughness, calculate a first reflection direction of an incident light ray at the surface vertex coordinates in combination with the refractive index, and randomly shift the first reflection direction based on the microsurface normal perturbation range; The energy attenuation value in the first reflection direction is calculated according to the radiation intensity of the artificial light source and the path length of the light propagation path.

[0010] Optionally, the triggering of the edge GPU node bound to the corresponding spatial block to perform a secondary calculation of parallel ray tracing, returning the secondary calculation result to the main GPU node for local frame synthesis, and generating an updated rendering block includes: According to the boundary coordinates of the corresponding spatial block, the updated lightweight component data is divided into a plurality of rendering subtasks, and the rendering subtasks are distributed to edge GPU nodes bound to the corresponding spatial block; In the edge GPU node, a local light propagation path is constructed based on the geometric data and illumination parameters of the corresponding spatial block, and light energy distribution of the local light propagation path is calculated; Returning the light energy distribution to the main GPU node, performing local frame synthesis on the light energy distribution data according to the boundary coordinates, and generating local frame buffer data; Perform edge smoothing processing on the local frame buffer data and the frame buffer data of adjacent spatial blocks to generate an updated rendering block.

[0011] Optionally, encapsulating the diffuse reflection coefficient, the specular reflection intensity, and the energy attenuation value into physical rendering parameters bound to the surface vertex coordinates, and removing redundant vertex data in the multi-level detail geometric model that is not affected by light to generate lightweight component data carrying physical rendering parameters, including: Generate a physical rendering parameter data block corresponding to the surface vertex coordinates according to the numerical ranges of the diffuse reflection coefficient, the specular reflection intensity, and the energy attenuation value; Based on the light visibility detection result of the surface vertex coordinates, screen out the coordinate data that is not affected by light, and mark the coordinate data that is not affected by light as redundant vertex data; Bind the physical rendering parameter data block to the surface vertex coordinates to generate a set of vertex data carrying physical rendering parameters; Remove the redundant vertex data from the multi-level detail geometric model, optimize the set of vertex data according to the removal result, and generate the lightweight component data carrying physical rendering parameters.

[0012] Optionally, based on the functional attribute division rule of building components, parse the design parameters into geometric topology data of multiple independent components, and dynamically generate a multi-level detail geometric model for each independent component according to the terminal display resolution and the viewing point distance, including: According to the functional attribute division rule of building components, extract independent component data corresponding to walls, beams, columns, doors, and windows from the design parameters to generate initial geometric topology data; Based on the terminal display resolution and the current viewing point distance, calculate the display detail level of the independent component data, and generate geometric simplification parameters matching the display detail level; According to the geometric simplification parameters, perform vertex merging and patch simplification on the initial geometric topology data to generate optimized geometric topology data; Associate and store the optimized geometric topology data with the functional attribute division rule, and dynamically generate a multi-level detail geometric model for each independent component.

[0013] In a second aspect, the present application provides a WebGL-based cloud collaborative three-dimensional building modeling and rendering system, including: A parsing module, configured to obtain design parameters of a target building from a cloud database, parse the design parameters into geometric topology data of multiple independent components based on the functional attribute division rule of building components, and dynamically generate a multi-level detail geometric model for each independent component according to the terminal display resolution and the viewing point distance; A calculation module, configured to attach material physical attributes to the surface vertex data of the multi-level detail geometric model, calculate the diffuse reflection coefficient and specular reflection intensity of the material physical attributes and the building scene lighting conditions through a ray tracing algorithm, and generate lightweight component data carrying physical rendering parameters; A synthesis module, configured to respond to a perspective switching instruction of a terminal user, allocate a main GPU node from a distributed GPU cluster according to the spatial block corresponding to the current perspective, the main GPU node divides the lightweight component data according to the spatial block, and allocates adjacent blocks of the spatial block to edge GPU nodes, the edge GPU nodes perform parallel ray tracing calculations on the allocated blocks based on the WebGL protocol, return the calculation results to the main GPU node, synthesize a terminal display image and display it.

[0014] In a third aspect, the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a WebGL-based cloud collaborative three-dimensional building modeling and rendering method as described in the first aspect above.

[0015] In a fourth aspect, the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a WebGL-based cloud collaborative three-dimensional building modeling and rendering method as described in the first aspect.

[0016] In an embodiment of the present application, design parameters of a target building are obtained from a cloud database, the design parameters are parsed into geometric topology data of multiple independent components based on a division rule of functional attributes of building components, and a multi-level detail geometric model of each independent component is dynamically generated according to the terminal display resolution and the viewpoint distance; material physical attributes are attached to the surface vertex data of the multi-level detail geometric model, the diffuse reflection coefficient and specular reflection intensity of the material physical attributes and the building scene lighting conditions are calculated through a ray tracing algorithm, and lightweight component data carrying physical rendering parameters is generated; in response to a perspective switching instruction of a terminal user, a main GPU node is allocated from a distributed GPU cluster according to the spatial block corresponding to the current perspective, the main GPU node divides the lightweight component data according to the spatial block, and allocates adjacent blocks of the spatial block to edge GPU nodes, the edge GPU nodes perform parallel ray tracing calculations on the allocated blocks based on the WebGL protocol, return the calculation results to the main GPU node, synthesize a terminal display image and display it.

[0017] The technical solution of the present application has the following beneficial effects: Design parameters are parsed through the functional attribute rules of building components, and the complex building model is disassembled into geometric topology data of independent components. Combining with the terminal display characteristics, a multi-level detail model is dynamically generated to adapt to different viewing distance and resolution requirements, and the geometric data load is optimized. Physical material properties are bound to the model vertex data, and the diffuse and specular reflection characteristics of the material and light are accurately calculated through the ray tracing algorithm to generate lightweight component data, ensuring the balance between physical rendering accuracy and data transmission efficiency. The space blocks are dynamically divided according to the user's perspective, and the distributed cluster resources are scheduled by the main GPU node. The edge nodes perform parallel ray tracing calculations on adjacent blocks based on the WebGL protocol, improving the rendering efficiency and reducing the load on the main node. The main node integrates the distributed calculation results, quickly generates high-fidelity images, supports real-time frame updates when the user's perspective is switched, and ensures smooth interaction.

[0018] Furthermore, parameters such as surface roughness and refractive index are extracted by matching the material database, a ray propagation path associated with the vertex is constructed, and the ray sampling density in the high-curvature area is dynamically adjusted to improve the detail accuracy. After calculating the first reflection direction and energy attenuation value, the physical rendering parameters bound to the vertex are encapsulated, and the redundant vertices not affected by light are removed. Finally, lightweight and high-precision component data is generated. Through the dynamic ray sampling optimization and redundant vertex removal mechanism, while retaining the material realism, the calculation and transmission load are significantly reduced, realizing the coordination of high-precision physical rendering and real-time interaction, and significantly improving the rendering efficiency and visual effect of complex building scenes.

[0019] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0021] Figure 1 Shows a flowchart of a WebGL-based cloud collaborative three-dimensional building modeling and rendering method provided by the present application; Figure 2 Shows a schematic structural diagram of a WebGL-based cloud collaborative three-dimensional building modeling and rendering system provided by the present application; Figure 3 Shows a schematic structural diagram of a computing device provided by the present application. Detailed Description of the Embodiments

[0022] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application.

[0023] In some processes described in the specification, claims, and the above-mentioned accompanying drawings of this application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are different types.

[0024] The R & D idea of this application is as follows: First, decouple building components through functional attribute rules, parse the cloud design parameters into geometric topology units that can be independently processed, and dynamically generate multi-level detail models in combination with the terminal display characteristics to adapt to different viewing distance accuracy requirements; then fuse the physical properties of materials at the vertex level, accurately calculate the lighting interaction effect based on the ray tracing algorithm, and generate lightweight and physically realistic rendering data; for real-time interaction requirements, design a dynamic segmentation strategy for spatial blocks, intelligently schedule distributed computing power by the main GPU node, and implement parallel ray tracing calculations for adjacent blocks through the WebGL protocol. Finally, efficiently synthesize high-fidelity images through the main node, forming a full-link optimization closed loop from data parsing, dynamic modeling, physical calculation to distributed rendering.

[0025] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0026] Figure 1 The flowchart of a WebGL-based cloud collaborative 3D building modeling and rendering method provided for the embodiments of this application is as Figure 1 shown, and this method includes: 101. Obtain the design parameters of the target building from the cloud database, parse the design parameters into geometric topology data of multiple independent components based on the functional attribute division rules of the building components, and dynamically generate multi-level detail geometric models of each independent component according to the terminal display resolution and viewing distance; In this step, the functional attribute division rule refers to the classification logic established based on the actual functions of building components in the engineering structure (such as load-bearing, enclosure, and decoration), which is used to disassemble the global design parameters into geometric data units of independent components. For example, "structural columns" and "curtain wall glasses" in the BIM model are separated into independent data entities. The multi-level detail geometric model is a progressive geometric model dynamically generated according to the terminal hardware performance and the user's viewing distance, containing multi-level data from high-precision (close view) to low-precision (distant view), and realizing the dynamic balance of storage and calculation loads through vertex cluster merging and patch simplification.

[0027] In the embodiment of the present application, after extracting the IFC format design parameters from the cloud BIM database, first, the component types are identified based on semantic tags (such as IfcWall, IfcSlab), and an independent topological structure is constructed according to the preset component function weights (detail retention is prioritized for load-bearing components). For each independent component, a dynamic LOD generation strategy based on frustum projection is adopted: by calculating the product of the terminal screen pixel density (PPI) and the viewing point distance, the geometric simplification intensity coefficient is deduced, and the edge collapse algorithm is driven to iteratively merge triangular patches, and finally, the multi-level detail geometric model of each independent component is generated.

[0028] Suppose a BIM model of a super high-rise building contains 280 million vertices. According to the functional attribute division rule, it is disassembled into independent components such as the core tube (structure), glass curtain wall (enclosure), and ventilation duct (equipment). When the user views it on a tablet computer (viewing distance 50 cm, resolution 2560×1600), the LOD level of the core tube is 5 (90% vertices are retained), and the LOD level of the curtain wall is 3 (40% vertices are retained); after switching to a workstation (viewing distance 30 cm, 8K resolution), the core tube automatically upgrades to the LOD level 8 (98% vertices), and the curtain wall upgrades to the LOD level 6 (85% vertices), generating the multi-level detail geometric model of each independent component.

[0029] 102. Attach the material physical attributes to the surface vertex data of the multi-level detail geometric model, and calculate the diffuse reflection coefficient and specular reflection intensity of the material physical attributes and the building scene lighting conditions through the ray tracing algorithm to generate lightweight component data carrying physical rendering parameters; In this step, the material physical attributes are a parameter set describing the optical response characteristics of building materials, including anisotropic roughness, subsurface scattering coefficient, Fresnel reflectivity, etc., which are used to simulate the interaction behavior of light and matter in the real world. The lightweight component data is a structured data packet after physical rendering calculation and redundancy elimination, containing a compact encoding format of geometric vertices, material parameters, and lighting pre-calculation results, supporting direct access and streaming transmission by the GPU.

[0030] In the application example, the building material knowledge graph is called to match the component type with the predefined PBR material template (such as the anisotropic highlights of marble and the refraction and transmission of glass). A hybrid algorithm of Photon Mapping and Bidirectional Path Tracing is used to calculate the lighting parameters in two stages: in the first stage, energy particles are propagated in the scene through a photon emitter, and the radiant flux of the photon collision points on the component surface is recorded; in the second stage, a spherical probe is constructed centered on the vertex, the photon density distribution within the probe range is collected, and the diffuse reflection coefficient is calculated in combination with the micro-surface heightfield model. The specular reflection intensity is estimated by the Ray Differentials method to calculate the influence of the surface curvature change on the highlight range. Finally, the vertices with photon density lower than the threshold are marked as "light inaccessible" and removed, generating lightweight component data carrying physical rendering parameters.

[0031] For example, continuing the above example, the super high-rise curtain wall components use low-iron glass material (refractive index 1.52, surface roughness 0.05). 10^6 photons are projected into the scene in the photon mapping stage, and significant differences in the photon density of the curtain wall vertices are detected: the average number of photons captured by the vertices on the sunny side is 120 photons / ㎡, while on the shady side it is only 3 photons / ㎡. After verification by bidirectional path tracing, 85% of the vertices on the shady side are removed, and the lightweight data volume is compressed from the original 1.2GB to 210MB, outputting lightweight component data carrying physical rendering parameters.

[0032] 103. In response to the perspective switching instruction of the end user, according to the spatial block corresponding to the current perspective, a main GPU node is allocated from the distributed GPU cluster. The main GPU node divides the lightweight component data according to the spatial block, and allocates the adjacent blocks of the spatial block to the edge GPU nodes. The edge GPU nodes perform parallel ray tracing calculations on the allocated blocks based on the WebGL protocol, return the calculation results to the main GPU node, and synthesize and display the terminal display image.

[0033] In this step, the spatial block division divides the three-dimensional building model into regular grid cells (such as 2m×2m×2m) according to the spatial coordinate system, and each cell is assigned to a computing node as an independent rendering task. The parallel ray tracing calculation is based on a distributed rendering framework with a heterogeneous computing architecture, and multi-GPU collaborative ray tracing acceleration is achieved through task sharding, data locality optimization, and asynchronous communication.

[0034] In the embodiments of the present application, after receiving the view parameter, the main GPU node determines the visible space block by using frustum culling and occlusion culling, and encodes the spatial continuity of the block according to the Morton Code. The work stealing task scheduling strategy is adopted to dynamically allocate the blocks to the edge GPU nodes: after each node loads the lightweight data of the corresponding block, a hierarchical bounding volume hierarchy (HLBVH) acceleration structure is constructed, and based on the wavefront ray tracing technology, the rays are grouped by material type (diffuse group, specular group, transmission group) for batch processing. For transparent materials (such as glass), stochastic alpha testing is used to eliminate aliasing artifacts; the change of dynamic light sources is reduced by temporal photon reuse to reduce the computational cost. After each node completes local rendering, the main node uses weighted variance antialiasing to fuse the sub-frame images, and ensures the multi-terminal display consistency through gamut adaptive mapping.

[0035] For example, continuing with the above example, when the user flies around a super high-rise building for browsing, the main node divides the visible area into 320 blocks (2m³). Edge node A is responsible for block X50-Y70-Z120 (curtain wall area). After loading 210MB of lightweight data, it completes 2.5×10^6 ray tracings within 12ms (the specular group accounts for 68%). The main node fuses all block data and enables gamut mapping (sRGB→DCI-P3), and finally outputs an HDR image on an 8K monitor with a peak brightness of 1000 nit.

[0036] In order to achieve real-time and accurate update of local changes and efficient utilization of rendering resources in the multi-user collaborative editing scenario of large buildings, the present application proposes a bounding box-driven incremental rendering pipeline, which constructs an end-to-end low-latency update link through change area detection, physical light recalculation, heterogeneous parallel rendering, and differential stream transmission. In some embodiments, it further includes: 201. When the multi-user collaborative editing operation triggers the change of the geometric shape or material property of at least one independent component, extract the three-dimensional spatial bounding box range of the changed component; In step 201, the three-dimensional spatial bounding box range is a cubic space range defined by the extreme values of the vertex coordinates of the changed component, which is used to identify the physical area that needs to recalculate the light and rendering.

[0037] In the embodiments of the present application, collaborative editing operations are monitored through an event bus, the component modification instructions submitted by the user (such as vertex displacement vectors, material reflectivity adjustment values) are parsed, and an accurate bounding box is constructed using the Hierarchical Bounding Volumes (HBV) iterative contraction algorithm: First, the changed area is quickly located based on the octree spatial index of the original geometry of the component. Subsequently, combined with the Markov Chain Monte Carlo (MCMC) sampling strategy, a probability assessment is made on the range of indirect lighting effects that may be caused by changes in material properties (for example, highly reflective materials may affect adjacent walls), and the bounding box boundary is dynamically extended to cover the direct and indirect scopes of action. Finally, the range of the three-dimensional space bounding box is output.

[0038] 202. Based on the updated vertex coordinate values and material property change values within the range of the three-dimensional space bounding box, recalculate the diffuse reflection coefficient and specular reflection intensity of the changed component under the lighting direction of the building scene, and generate lightweight component data carrying physical rendering parameters after update; In step 202, the updated vertex coordinate values and material property change values are a set of geometric vertex spatial positions or material optical characteristic parameters directly modified by the user's editing behavior.

[0039] In the embodiments of the present application, within the bounding box space, a hybrid strategy of Multiple Importance Sampling (MIS) and Virtual Point Lights (VPLs) is used to reconstruct the local lighting field: Virtual point lights are projected along the main light source direction of the building scene (such as the solar azimuth angle) onto the surface of the bounding box to construct a light energy transmission proxy network; The Stratified Russian Roulette is used to terminate low-contribution light paths and focus on calculating high-energy transmission paths; The anisotropic Ward reflection model is applied to the changed material, and a normal perturbation map is generated in combination with the micro-surface height field to correct the azimuth distribution of diffuse reflection and specular reflection, so as to recalculate the diffuse reflection coefficient and specular reflection intensity. Finally, vertex data with a contribution degree lower than the threshold is removed through Flux Clustering, and lightweight component data carrying physical rendering parameters after update is generated.

[0040] 203. According to the corresponding spatial block within the range of the three-dimensional space bounding box, distribute the updated lightweight component data to the distributed GPU cluster, trigger the edge GPU node bound to the corresponding spatial block to perform parallel ray tracing secondary calculation, and return the secondary calculation result to the main GPU node for local frame synthesis to generate an updated rendering block; In step 203, the corresponding spatial block is a regular three-dimensional grid cell formed by dividing the building model space, and each cell is bound to a specific GPU computing node to achieve task load balancing.

[0041] In the embodiment of the present application, the master node quickly retrieves the set of blocks covered by the bounding box based on the spatial block hash mapping table, and dynamically allocates tasks using the priority queue scheduling strategy: high-visual-significance blocks (such as around the user's fixation point) are preferentially allocated to high-performance edge nodes. After receiving the data, the edge node starts the asynchronous compute shaders to construct a hybrid acceleration structure: only reconstruct the bottom-level acceleration structure (BLAS) of the geometry inside the bounding box, and reuse the global top-level acceleration structure (TLAS); bucket by ray type (primary ray, shadow ray, reflection ray), and use wavefront thread scheduling to maximize GPU utilization; combine temporal accumulation and wavelet denoising in the spatial domain to generate a low-noise intermediate frame. The master node uses the visibility pyramid to fuse the sub-frames of each block to generate an updated rendered block.

[0042] 204. Perform a pixel-level difference comparison between the updated rendered block and the original display data in the unchanged area, extract the difference pixel coordinates and color values, and overwrite the original display data through the difference data stream.

[0043] In step 204, the difference data stream is an incremental update data packet that only contains the changed pixel positions and color information, and is used to minimize the network transmission and terminal rendering overhead.

[0044] In the embodiment of the present application, a multi-modal difference detection pipeline is adopted: apply the structural similarity index (SSIM) and the human visual system (HVS) weighted model to filter out tiny color differences that are invisible to the naked eye (such as ΔE < 1.5); calculate the pixel displacement vector between the front and back frames through the block matching algorithm to correct the pseudo-differences caused by the perspective fine-tuning; compress the set of difference pixels using adaptive sparse coding and perceptual hashing to generate a high-compression-ratio difference stream, and overwrite the original display data.

[0045] The following is a specific example: When an international team collaborated on the design of a twin-tower landmark building, User A adjusted the geometric curvature of the curved glass curtain wall of the observation deck at the top of the tower from a radius of 15 m to 12 m (involving a displacement of 38,000 vertices), and User B simultaneously changed the curtain wall material from ordinary glass (refractive index 1.5, roughness 0.1) to electrochromic glass (refractive index dynamic range 1.4 - 1.7, roughness 0.05). Through Step 201, the initial bounding box (X200 - Y300 - Z400 to X220 - Y320 - Z420) was generated for the changed area of the curtain wall, and the indirect influence range was evaluated by combining the light scattering characteristics of the electrochromic material, and the bounding box was extended to X190 - Y290 - Z390 to X230 - Y330 - Z430; through Step 202, 25,000 virtual point light sources were arranged within the extended bounding box, and the specular highlight diffusion effect caused by the curvature change was calculated using the anisotropic Ward model to generate updated lightweight component data (the number of vertices was compressed from 480,000 to 220,000); through Step 203, the 24 spatial blocks corresponding to the bounding box were assigned to 4 edge nodes equipped with NVIDIA A40 GPUs, and the secondary ray tracing was completed within 18 ms using a hybrid acceleration structure, and the local sub-frame with a signal-to-noise ratio (SNR) of 42 dB was output and synthesized after spatio-temporal noise reduction to generate an updated rendering block; through Step 204, 157,000 different pixels (accounting for 1.2% of the total pixels) were detected, and the data volume was reduced to 1.8 MB after sparse coding compression. The terminal completed the screen update within 3 ms through the WebGL pixel shader to overwrite the original display data.

[0046] This application realizes high-precision real-time feedback for local updates in a multi-user collaborative editing scenario through an incremental rendering pipeline driven by a bounding box. The change area detection combined with indirect influence evaluation ensures the physical completeness of the lighting recalculation; the heterogeneous distributed rendering framework outputs high-quality images with low latency through a hybrid acceleration structure and spatio-temporal noise reduction; the differential flow mechanism significantly reduces the network transmission load, enabling the terminal to achieve pixel-level precise updates on a high-resolution screen. This solution significantly improves the efficiency of collaborative design of large buildings, enabling designers to synchronously modify and verify the lighting and material effects of complex structures without perceptible latency, while avoiding resource waste caused by global rendering.

[0047] To achieve the collaborative optimization of high-precision physical rendering and data lightweighting in 3D building modeling, this application proposes a material-driven adaptive lighting calculation framework, which constructs a complete link from material binding to rendering output through accurate mapping of material parameters, optimization of light paths, dynamic adjustment of sampling density, and data encapsulation and compression. In some embodiments, physical material properties are attached to the surface vertex data of the multi-level detail geometric model, and the diffuse reflection coefficient and specular reflection intensity of the physical material properties and the lighting conditions of the building scene are calculated through a ray tracing algorithm to generate lightweight component data carrying physical rendering parameters, including: 301. Match the material type corresponding to the multi - level detail geometric model in the building material database, extract the surface roughness, refractive index, and metallicity parameters of the material type, and map them to the surface vertex data of the multi - level detail geometric model. In step 301, the material type is a predefined standardized material category in the building material database (such as granite, tempered glass, anodized aluminum), which includes a parametric description of its physical and optical properties.

[0048] In the embodiment of the present application, a material matching engine based on a graph neural network (GNN) is called to perform graph - structure modeling on the surface topological features (curvature distribution, triangle patch density) of the multi - level detail geometric model. Similarity retrieval is performed between the node embedding vectors and the feature maps in the material database to match the best material type. For the matching result, non - uniform B - spline interpolation (NURBS) is used to map the discrete material parameters (such as surface roughness, refractive index, metallicity parameters) to vertex attributes: the vertices in the high - curvature region are sharpened, and the vertices in the flat region are Gaussian - smoothed to ensure natural material transition. Finally, the parameters are mapped to the surface vertex data of the multi - level detail geometric model through vertex shader channel encoding.

[0049] 302. Based on the preset natural light direction and artificial light source position in the building scene, construct a light propagation path associated with the surface vertex data of the multi - level detail geometric model, and calculate the first - reflection direction and energy attenuation value of each light path at the surface vertex coordinates of the multi - level detail geometric model according to the surface roughness and the refractive index. In step 302, the light propagation path is the light trajectory emitted from the light source to the surface vertex and undergoing reflection / refraction, and its direction and energy distribution are jointly determined by the material properties and the scene geometry.

[0050] In the embodiments of the present application, a light transport network based on a hybrid of bidirectional path tracing and virtual point light sources (VPLs) is constructed: light rays are synchronously emitted from the natural light direction (solar altitude angle) and the artificial light source position (lamp coordinates), the ray differentials technique is used to estimate the influence of surface curvature on light diffusion, and a light propagation path associated with the surface vertex data is constructed. For the calculation of the first reflection direction, the anisotropic microfacet model is applied, and it is calculated after generating a normal perturbation field by combining the azimuthal dependence of the surface roughness; the energy attenuation value is obtained by quantifying the scattering loss of light rays inside the material through volumetric photon mapping, and a dispersion compensation coefficient is introduced for the transmissive material (such as glass) to eliminate the wavelength-dependent attenuation deviation.

[0051] 303. Dynamically adjust the light sampling density for the surface vertex coordinates, where the vertices in the high-curvature region increase the number of secondary light ray samplings, and the vertices in the flat region decrease the number of secondary light ray samplings, to generate the diffuse reflection coefficient and specular reflection intensity of the surface vertex coordinates; In step 303, the light sampling density is the number of secondary light rays projected per unit area, which is used to balance the rendering quality and the computational cost.

[0052] In the embodiments of the present application, an adaptive multiple importance sampling (AMIS) strategy is adopted to dynamically allocate sampling resources: a sampling weight map is generated based on the curvature entropy and the local lighting gradient, hierarchical quasi-Monte Carlo sampling is initiated for the high-curvature region (such as carved decorations) to increase the number of secondary light rays in the specular reflection direction; for the flat region (such as the wall surface), a low-discrepancy sequence is used to reduce the sampling density. Through the variance-aware termination mechanism, the sampling convergence state is detected in real time and the low-contribution light rays are terminated in advance to generate the diffuse reflection coefficient and specular reflection intensity.

[0053] 304. Package the diffuse reflection coefficient, the specular reflection intensity, and the energy attenuation value into physical rendering parameters bound to the surface vertex coordinates, and remove the redundant vertex data in the multi-level detail geometric model that is not affected by light, to generate lightweight component data carrying the physical rendering parameters.

[0054] In step 304, the physical rendering parameters are a structured data set describing the vertex light response characteristics, including the diffuse reflection coefficient, the specular reflection intensity, and the energy attenuation value.

[0055] In the embodiment of the present application, the Radiant Flux Clustering algorithm is applied to compress and optimize the surface vertex coordinates: a k-d tree spatial index is constructed based on the vertex radiation contribution degree, hierarchical clustering is performed on the low-radiant flux vertices (such as shadow areas and backlit surfaces), similar vertices are merged, and redundant vertex data not affected by light is removed. After the physical rendering parameters are compressed by entropy encoding, they are stored in a bit-interleaved manner with the surface vertex coordinates to generate lightweight component data carrying the physical rendering parameters.

[0056] The following is a specific example: In a large stadium BIM collaborative design project, the steel structure roof uses a multi-level detail geometric model (LOD level 5: 2 million vertices). Through step 301, the GNN engine identifies the roof surface characteristics, matches the material as galvanized steel (roughness 0.25, metallicity 0.95), and generates vertex-level parameters through NURBS interpolation. The roughness of the welded joint area with a curvature greater than 0.8 is increased to 0.4; through step 302, the preset natural light direction is 30 degrees south by east, the artificial light source is 200 groups of LED arrays, 120 million light paths are generated by bidirectional path tracing, and the volume photon mapping shows that the energy attenuation value in the backlit area reaches 68%; through step 303, the vertex curvature entropy in the arch area exceeds the threshold, and hierarchical quasi-Monte Carlo sampling is started. The number of secondary rays is increased to 512 rays / vertex in the flat area and decreased to 64 rays / vertex in the flat area, and 23% of the low-contribution rays are terminated in advance by variance detection; through step 304, 38% of the redundant vertices are removed by radiant flux clustering, and the data volume is reduced from 15 GB to 3.8 GB after entropy encoding compression, generating lightweight component data, which is streamed to the terminal through WebGL.

[0057] This application realizes the collaborative optimization of the physical rendering accuracy and data efficiency of building models through a material-driven adaptive lighting framework. The accurate mapping of material parameters and the optimization of light paths ensure the realistic lighting response of complex geometric surfaces; the dynamic sampling strategy significantly reduces the computational overhead; the clustering of radiant flux and entropy coding compression reduces the data volume to 1 / 4 of the traditional solution.

[0058] This application realizes the collaborative optimization of the physical rendering accuracy and data efficiency of building models through a material-driven adaptive lighting framework. The accurate mapping of material parameters and the optimization of light paths ensure the realistic lighting response of complex geometric surfaces; the dynamic sampling strategy significantly reduces the computational overhead; the clustering of radiant flux and entropy coding compression reduces the data volume to 1 / 4 of the traditional solution. In the stadium case, the system supports real-time rendering at 4K resolution. Designers can interactively adjust the material and light source parameters and instantly view the high-fidelity effects, greatly improving the design verification efficiency and the smoothness of collaborative work.

[0059] In order to achieve physically accurate reflection direction prediction and energy attenuation modeling in the lighting calculation of building scenes, this application proposes an energy transfer framework that couples reverse ray tracing and micro-surfaces. By parsing the light source parameters, constructing the reverse path, correcting the normal perturbation, and quantifying the attenuation, a complete energy transfer link from the light source to the surface vertices is established. In some embodiments, based on the preset natural light direction and the position of artificial light sources in the building scene, a light propagation path associated with the surface vertex data of the multi-level detail geometric model is constructed, and the first reflection direction and energy attenuation value of each light path at the surface vertex coordinates are calculated according to the surface roughness and the refractive index, including: 401. Read the preset natural light direction angle and the three-dimensional spatial coordinates of the artificial light source from the building scene configuration file, and obtain the radiant intensity and spectral distribution data of the artificial light source; In step 401, the natural light direction angle is the azimuth and elevation angle parameters of the sunlight incident direction, which are used to define the spatio-temporal distribution characteristics of natural light in the building scene. The three-dimensional spatial coordinates are the spatial position data of the artificial light source (such as lamps, projection devices) in the building scene.

[0060] In the embodiments of this application, the building scene configuration file in JSON format is parsed, and the natural light direction angle (such as the solar azimuth angle accurate to 0.01 degrees) and the artificial light source coordinates (XYZ axis accurate to the millimeter level) are extracted through a semantic segmentation engine. For artificial light sources, a Spectroradiometric Calibration Model is used to parse their radiant intensity (unit: watt / steradian) and spectral distribution data (energy density curve in the 380nm - 780nm band).

[0061] 402. Starting from the surface vertex coordinates, extend the light propagation path in the opposite direction of the natural light direction angle and the three-dimensional space coordinates to establish the direct illumination association relationship between the surface vertex coordinates and the artificial light source. In step 402, extending the light propagation path in the opposite direction traces the light path reversely from the surface vertex towards the light source direction, which is used to detect the direct illumination accessibility between the vertex and the light source.

[0062] In the embodiment of the present application, an inverse ray tracing acceleration structure is constructed based on the surface vertex coordinates: First, use a Sparse Voxel Octree (SVO) to divide the building scene in space. Emit reverse rays (the direction is the opposite direction of natural light and the artificial light source coordinate direction) at the vertex, and use Conservative Rasterization to detect the potential intersections of the rays with the scene geometry. For the artificial light source direction, apply the Visibility Cone algorithm to quickly exclude the occluded paths and only retain the effective rays that reach the light source directly. Finally, generate the direct illumination association relationship between the vertex and the light source.

[0063] 403. Determine the micro-surface normal perturbation range at the surface vertex coordinates according to the surface roughness, calculate the first reflection direction of the incident light at the surface vertex coordinates in combination with the refractive index, and perform a random offset on the first reflection direction based on the micro-surface normal perturbation range. In step 403, the micro-surface normal perturbation range is the random offset angle interval of the normal direction determined by the surface roughness, which is used to simulate the influence of the microscopic geometry of the material on the light reflection direction.

[0064] In the embodiment of the present application, the Anisotropic Beckmann Distribution is used to model the micro-surface normal perturbation: Generate the normal direction covariance matrix according to the surface roughness parameters, and define the standard deviation of the normal in the tangent plane and the normal direction. Combine the refractive index parameters, calculate the ideal reflection direction vector through Snell's Law, and perform a random offset within the perturbation range based on Markov Chain Monte Carlo (MCMC) sampling. For high-roughness surfaces (such as concrete), start the Stratified Importance Sampling to increase the diversity of the perturbation directions; for low-roughness surfaces (such as mirror metals), use the Quasi-Random Sequence to ensure the uniformity of the direction distribution.

[0065] 404. Calculate the energy attenuation value of the first reflection direction according to the radiation intensity of the artificial light source and the path length of the light propagation path.

[0066] In step 404, the path length is the propagation distance of the light ray from the light source to the surface vertex, which is used to quantify the spatial attenuation of energy during transmission.

[0067] In the embodiments of the present application, double attenuation calculation is applied using the Atmospheric Transmittance Model and the Material Absorption Model: Based on the path length and the preset turbidity factor of the scene, the Hosek-Wilkie sky model is used to calculate the scattering loss of light in the atmosphere; according to the material type (such as glass, metal), the preset absorption spectral curve is queried, and the energy attenuation rate is calculated by integrating along the path length; the atmospheric transmittance and the material absorptance are multiplied, combined with the radiation intensity of the artificial light source, and the energy attenuation value in the first reflection direction is output.

[0068] The following is a specific example: In a dome lighting design project of a certain museum, through step 401, the natural light direction angles (azimuth angle 120°, elevation angle 45°) and the coordinates of 36 groups of artificial spotlights (XYZ error ±2 mm) are obtained by parsing the configuration file. Spectral analysis shows that the main peak wavelength of the spotlight is 560 nm (warm white light), and the average value of the radiation intensity data is 850 W / sr; through step 402, backward light rays are emitted from the dome surface vertices (about 500,000 vertices). The SVO acceleration structure excludes the paths blocked by the decorative reliefs, and a direct connection diagram of the direct light reachability is generated, showing that 82% of the vertices can receive direct illumination from at least 3 spotlights; through step 403, the surface roughness of the bronze relief is 0.6, and the Beckmann distribution is used to generate a normal perturbation range of ±15°. MCMC sampling generates 1024 offset directions, and the main lobe width of the specular reflection is expanded to 3 times the original value; through step 404, the average path length from the spotlight to the dome vertex is 8.5 m, the atmospheric transmittance is 0.92, and the absorptance of the bronze material is 0.85. Finally, it is calculated that the energy in the first reflection direction is attenuated to 78.2% of the original value.

[0069] The present application realizes the balance between the physical accuracy and the calculation efficiency of the building scene illumination calculation through an energy transmission framework that couples backward optical path tracing and micro-surfaces. The high-precision parsing of the light source parameters and the backward path acceleration structure ensure the reliability of the direct light illumination detection; the combination of the Beckmann distribution and MCMC sampling improves the authenticity of the rough surface reflection direction modeling; the double attenuation model of the atmosphere and the material quantifies the details of the energy loss.

[0070] To achieve efficient rendering and visual continuity guarantee for local updates of building models in a distributed GPU cluster, this application proposes a spatially-block-driven heterogeneous parallel rendering pipeline. By means of task sharding, local light path calculation, sub-frame fusion, and edge optimization, a complete rendering link from data distribution to image synthesis is constructed. In some embodiments, triggering the edge GPU nodes bound to the corresponding spatial block to perform parallel ray tracing secondary calculations, and returning the secondary calculation results to the main GPU node for local frame synthesis to generate updated rendering blocks, including: 501. Divide the updated lightweight component data into multiple rendering subtasks according to the boundary coordinates of the corresponding spatial block, and distribute the rendering subtasks to the edge GPU nodes bound to the corresponding spatial block; In step 501, the boundary coordinates of the corresponding spatial block are a three-dimensional grid cell of a fixed size obtained by pre-dividing the building model, and each cell is a cubic space range defined by its minimum / maximum XYZ coordinates.

[0071] In the embodiments of this application, the master node performs topological sorting on the updated lightweight component data based on the Morton Code space-filling curve to ensure spatial locality in the storage of adjacent block data. A dynamic load balancing strategy is adopted: the complexity weights of each block are calculated through a historical rendering time prediction model, combined with the real-time GPU utilization rate (CUDA core occupancy rate, remaining video memory capacity) of the edge nodes, and the improved Hungarian Algorithm is used to achieve the optimal matching of tasks and nodes. When distributing tasks, the zero-copy RDMA (Remote Direct Memory Access) technology is adopted to directly write the updated lightweight component data into the video memory address space of the edge GPU nodes.

[0072] 502. In the edge GPU nodes, construct a local light propagation path based on the geometric data and lighting parameters of the corresponding spatial block, and calculate the light energy distribution of the local light propagation path; In step 502, the local light propagation path is a set of light trajectories limited within the spatial block, including the complete transmission path from the light source to the surface vertex and then to the camera.

[0073] In the embodiments of the present application, the edge node starts a hybrid acceleration structure construction pipeline: constructs a bottom - layer acceleration structure (BLAS) for the geometric data within the block, and at the same time re - uses the global top - layer acceleration structure (TLAS) to implement cross - block ray tracing. The wavefront tracing technology is used to group and schedule the rays according to the material type (diffuse, specular, transmissive): the diffuse group applies multiple importance sampling (MIS) to optimize the convergence speed of indirect illumination; the specular group uses an anisotropic micro - surface model to generate the highlight direction distribution; the transmissive group combines dispersion compensation and random alpha testing to eliminate the artifacts of transparent materials. Finally, the radiance cache records the contribution of each path and calculates the light energy distribution of the local light propagation path.

[0074] 503. Return the light energy distribution to the main GPU node, and perform local frame synthesis on the light energy distribution data according to the boundary coordinates to generate local frame buffer data; In step 503, the local frame buffer data is a set of pixels of the rendering result corresponding to the spatial block, including color, depth, and normal buffers.

[0075] In the embodiments of the present application, the main node uses a combination of temporal anti - aliasing (TAA) and spatial super - resolution to fuse multi - frame data: aligns the historical frame and the current frame based on the motion vector, and reduces noise through weighted averaging; applies edge - directed upsampling to the low - resolution block to reconstruct high - frequency details; uses a visibility pyramid to detect and eliminate depth conflicts and lighting discontinuities between adjacent blocks, and generates local frame buffer data.

[0076] 504. Perform edge smoothing on the local frame buffer data and the frame buffer data of adjacent spatial blocks to generate an updated rendering block.

[0077] In step 504, the edge smoothing process is a pixel - level optimization operation to eliminate visual discontinuities at the seams of adjacent spatial blocks.

[0078] In the embodiments of the present application, multi - stage edge smoothing is performed on the local frame buffer data and the frame buffer data: the seam region is identified based on the structural similarity index (SSIM) and depth gradient analysis; the depth - aware bilateral filter is applied to the detected region to smooth the color jump while retaining the geometric edges; the pixel displacement between adjacent frames in the dynamic scene is predicted by the optical flow method, and the filtering weights are corrected to avoid motion blur, and finally an updated rendering block is generated.

[0079] The following is a specific example: Suppose a user modifies the glass curtain wall material of the central dome (refractive index adjusted from 1.5 to 1.8) in a smart city transportation hub, triggering the following process: In step 501, the dome corresponds to 12 spatial blocks (X100 - Y200 - Z50 to X160 - Y260 - Z110). The master node, based on the performance differences between T4 / A40 nodes, assigns the high - curvature blocks to the A40 nodes and the low - complexity blocks to the T4 nodes; In step 502, the edge node A40 constructs a wavefront - tracking pipeline for the dome blocks, increasing the proportion of mirror - group light rays to 65%. It uses the micro - surface model to generate the refractive - direction distribution, completing 120 million ray tracings within 36 ms and calculating the ray - energy distribution; In step 503, the master node fuses the time - domain cumulative frame (weight 0.7) with the current frame (weight 0.3), and the visibility pyramid eliminates the depth conflict between the columns and the dome; In step 504, it detects and edge - smooths the color - temperature difference (transition zone from 6500K to 5500K) between the dome and the adjacent waiting - hall blocks, and the optical - flow compensation corrects the pixel - displacement artifacts caused by the train movement, generating the updated rendering blocks.

[0080] This application realizes the efficient distributed computing and visual seamless fusion of local updates of building models through a heterogeneous parallel rendering pipeline driven by spatial blocks. Morton - code sorting and Hungarian task scheduling ensure balanced computing loads; Wavefront tracking and hybrid acceleration structures improve ray - calculation efficiency; Time - domain - spatial joint anti - aliasing guarantees high - quality output; Depth - perception filtering and motion compensation eliminate edge defects.

[0081] In order to achieve efficient storage of physical parameters and precise elimination of redundant data in 3D building rendering, this application proposes a radiation - flux - driven data - compression optimization framework. Through parameter - block generation, visibility detection, data binding, and vertex elimination, it constructs a complete link from parameter encoding to lightweight data output. In some embodiments, encapsulating the diffuse - reflection coefficient, the specular - reflection intensity, and the energy - attenuation value as physical rendering parameters bound to the surface - vertex coordinates, and eliminating the redundant vertex data in the multi - level - of - detail geometric model that is not affected by light, generating lightweight component data carrying physical rendering parameters, including: 601. Generate physical - rendering - parameter data blocks corresponding to the surface - vertex coordinates according to the numerical ranges of the diffuse - reflection coefficient, the specular - reflection intensity, and the energy - attenuation value; In step 601, the physical - rendering - parameter data block contains a binary data structure of vertex - level light - response parameters (diffuse reflection, specular reflection, energy attenuation), which is segmented and encoded according to the numerical range.

[0082] In the embodiments of the present application, the range-adaptive encoding technology is adopted to quantize and compress the diffuse reflection coefficient, specular reflection intensity, and energy attenuation value: First, the vertices are grouped according to parameter similarity based on the K-means clustering algorithm, and the parameter values within each group are mapped to 8 / 16-bit precision through nonlinear quantization. Among them, high-dynamic-range parameters (such as specular reflection intensity > 0.9) use logarithmic encoding to retain details, and low-dynamic-range parameters (such as diffuse reflection coefficient < 0.2) use linear encoding. The encoded parameter blocks are bound to the surface vertex coordinates through bit-interleaving to form a physical rendering parameter data block that can be directly read by the GPU texture sampler.

[0083] 602. Based on the light visibility detection result of the surface vertex coordinates, filter out the coordinate data not affected by light, and mark the coordinate data not affected by light as redundant vertex data; In step 602, the light visibility detection result: a set of boolean flags for determining whether a vertex is irradiated by direct or indirect light sources.

[0084] In the embodiments of the present application, a multi-level light visibility detection pipeline is constructed: Use octree occlusion culling to quickly exclude vertices completely occluded by building components; Collect the photon density around the vertices through radiosity probes, and mark the vertices with a density lower than the noise threshold as invisible; Apply variance shadow mapping (VSM) to perform secondary verification on the candidate vertices to eliminate misjudgments. Finally, generate a redundancy bitmap, and mark the coordinate data not affected by light as redundant vertex data.

[0085] 603. Bind the physical rendering parameter data block to the surface vertex coordinates to generate a set of vertex data carrying physical rendering parameters; In step 603, the set of vertex data is a composite data structure that integrates geometric coordinates and physical parameters, supporting streaming transmission and direct GPU parsing.

[0086] In the embodiments of the present application, a spatial hash index is used to bind the physical rendering parameter data block to the surface vertex coordinates: three-dimensional hash bucketing is performed on the vertex coordinates, and the difference values of the physical rendering parameter data blocks in the same spatial bucket are compressed and stored through delta encoding. At the same time, to meet the dynamic update requirements, a versioned pointer mechanism is introduced, allowing the parameter block to be updated in place in the GPU video memory without affecting the stability of the coordinate data, and finally generating a vertex data set carrying physical rendering parameters.

[0087] 604. Remove the redundant vertex data from the multi-level detail geometric model, optimize the vertex data set according to the removal result, and generate the lightweight component data carrying physical rendering parameters.

[0088] In step 604, the lightweight component data is a compressed data packet of the valid vertices and their associated parameters remaining after removing the redundant vertices.

[0089] In the embodiments of the present application, a topology-aware vertex culling strategy is implemented: based on the redundant vertex marking bitmap, the graph cut algorithm is used to identify and remove isolated redundant vertex clusters while retaining the necessary topological connectivity (such as edge and face index). Entropy encoding and dictionary compression are applied to the remaining vertex data set to generate lightweight component data that supports real-time decompression by LZ4.

[0090] The following is a specific example: In a project to optimize the light and shadow effect of the curtain wall of a super high-rise office building, the following process is executed: In step 601, the specular reflection intensity of the curtain wall vertices (about 1.2 million) is distributed between 0.85 and 0.98, compressed to 8 bits using logarithmic encoding, and the data volume is reduced from 720MB to 90MB to construct a physical rendering parameter data block; in step 602, the octree removes the vertices in the backlight area (about 280,000), and the radiosity probe detects the vertices in the shadow area (about 150,000), and a total of 360,000 redundant vertex data are marked after the VSM secondary verification; in step 603, the spatial hash bucketing divides the curtain wall vertices into 512 buckets, and the delta encoding further compresses the volume of the parameter block by 22% to form a vertex data set; in step 604, the graph cut algorithm removes the redundant vertices and reconstructs the face topology, and the final lightweight component data after entropy encoding is 58MB, with a compression ratio of 92% compared to the original data.

[0091] Through the data compression optimization framework driven by radiation flux, the present application realizes the efficient storage and transmission of physical rendering data of building models. The range adaptive coding and spatial hash binding significantly reduce the parameter storage overhead; the multi-level visibility detection accurately identifies invalid vertices; the topology-aware culling ensures geometric integrity.

[0092] In order to achieve the dynamic optimization and multi-terminal adaptation of component-level geometric data in the building information model, the present application proposes a progressive geometric generation framework driven by functional semantics. By means of component semantic parsing, level of detail calculation, topology simplification and associated storage, a complete generation link from design parameters to multi-level models is constructed. In some embodiments, according to the functional attribute division rules of building components, the design parameters are parsed into geometric topology data of multiple independent components, and multi-level detailed geometric models of each independent component are dynamically generated according to the terminal display resolution and the viewing point distance, including: 701. According to the functional attribute division rules of building components, extract the independent component data corresponding to walls, beams, columns, doors and windows from the design parameters, and generate initial geometric topology data; In step 701, the functional attribute division rule is a data classification standard defined based on the structural role of building components in the project (such as load-bearing, enclosure, decoration), and is used to separate independent components from the global design parameters. The initial geometric topology data is the original geometric structure data without simplification, including vertex coordinates, edge connection relationships and patch indices.

[0093] In the embodiments of the present application, a semantic-enhanced graph convolutional network (SE-GCN) is used to parse BIM design parameters: first, the component attributes in IFC format (such as IfcWall, IfcColumn) are encoded as graph node features, and the semantic relationships of adjacent components are aggregated through multi-layer graph convolution to generate a component function weight vector. Based on the weight threshold segmentation, the geometric data of independent components such as walls, beams and columns are extracted, and the Delaunay Triangulation is applied to optimize the patch topology to eliminate self-intersection and non-manifold structures. Finally, the initial geometric topology data meeting the Watertight requirements is output.

[0094] 702. Based on the terminal display resolution and the current viewing point distance, calculate the display level of detail of the independent component data, and generate geometric simplification parameters matching the display level of detail; In step 702, the display level of detail is a model accuracy level dynamically divided according to the terminal performance and the observation conditions, and the larger the value, the richer the geometric details. The geometric simplification parameters are a set of parameters for controlling the vertex merging ratio, the patch simplification strength and the curvature retention threshold.

[0095] In the embodiments of the present application, an adaptive detail model based on the human visual system (HVS) is constructed: calculate the proportion of the projected area of the component in the screen space, and if it is lower than the pixel density threshold (such as 0.5 pixels / mm²), reduce the detail level; use the Weber-Fechner Law to quantify the perception threshold of the human eye for surface curvature changes, and dynamically adjust the simplification intensity of high-curvature regions; balance the simplification rate and visual fidelity through a multi-objective optimization algorithm (NSGA-II), and output geometric simplification parameters matching the display detail level.

[0096] 703. According to the geometric simplification parameters, perform vertex merging and facet simplification on the initial geometric topology data to generate optimized geometric topology data; In step 703, vertex merging aggregates vertices that are spatially adjacent and have similar attributes into a single vertex, reducing geometric data redundancy. Facet simplification reduces the model complexity by removing low-contribution triangular facets or merging coplanar facets.

[0097] In the embodiments of the present application, an iterative geometric simplification pipeline is implemented: evaluate the folding cost of each edge based on the QEM (quadratic error metric) algorithm, and preferentially fold the edges with the least visual impact; for high-curvature regions such as carvings and threads, apply Constrained Delaunay Refinement to retain feature edges; use an incremental mesh reconstruction algorithm to ensure that the simplified model maintains manifoldness and hole closure. Finally, optimized geometric topology data that meets the target detail level is generated.

[0098] 704. Associatively store the optimized geometric topology data with the functional attribute partitioning rules to dynamically generate a multi-level detail geometric model for each independent component.

[0099] In step 704, associative storage is a database structure that binds geometric data with the functional semantics of components for storage, supporting retrieval on demand and dynamic loading. The multi-level detail geometric model is a geometric data set containing multiple preset detail levels, supporting real-time switching to adapt to different terminals.

[0100] In the embodiments of the present application, a spatial multi-resolution index based on an octree is constructed: create an octree node for each independent component, and the leaf node stores the optimized geometric data corresponding to the LOD level; use Git-style version control to manage the change history of geometric data at different detail levels; according to the detail level requested by the terminal, pull the corresponding data block from the distributed storage in real time and assemble it into a complete model, and dynamically generate a multi-level detail geometric model for each independent component.

[0101] The following is a specific example: In a steel structure delivery project of a stadium, the following process is executed: In step 701, SE-GCN separates independent components such as grandstand trusses (functional weight 0.92), roof reticulated shells (0.85), and support columns (0.78) from the BIM model, and the generated initial geometric topology data contains 5.8 million vertices; in step 702, the mobile device (viewing distance 1m, 2K screen) requests LOD3 level, the simplification rate of the grandstand truss is set to 65%, and the characteristic edges with curvature > 0.05 of the roof reticulated shell are retained to obtain geometric simplification parameters; in step 703, QEM edge folding reduces the grandstand vertices to 2.03 million, and constrained Delaunay refinement retains the reticulated shell bolt hole features to generate optimized geometric topology data, and the data volume is reduced to 1.8GB; in step 704, the octree index loads LOD3 data to the terminal as needed, dynamically generates multi-level detail geometric models of each independent component, and engineers can seamlessly switch to LOD6 (4.5 million vertices) to view welding details.

[0102] This application realizes the intelligent optimization and efficient management of geometric data of building components through a function-semantics-driven progressive geometry generation framework. The semantic parsing and visual perception model ensure that key engineering features are retained during the simplification process; multi-resolution indexing and versioned storage support the real-time dynamic loading of models with hundreds of millions of vertices.

[0103] Figure 2 The following is a schematic structural diagram of a cloud-based collaborative 3D building modeling and rendering system based on WebGL provided for an embodiment of this application, as Figure 2 shown. The system includes: A parsing module 21, configured to obtain design parameters of a target building from a cloud database, parse the design parameters into geometric topology data of multiple independent components based on the functional attribute division rules of building components, and dynamically generate multi-level detail geometric models of each independent component according to the terminal display resolution and viewing distance; A calculation module 22, configured to attach material physical attributes to the surface vertex data of the multi-level detail geometric model, calculate the diffuse reflection coefficient and specular reflection intensity of the material physical attributes and the building scene lighting conditions through a ray tracing algorithm, and generate lightweight component data carrying physical rendering parameters; A synthesis module 23, configured to respond to a perspective switching instruction of a terminal user, allocate a main GPU node from a distributed GPU cluster according to the spatial block corresponding to the current perspective, the main GPU node divides the lightweight component data by spatial block, and allocates adjacent blocks of the spatial block to edge GPU nodes. The edge GPU nodes perform parallel ray tracing calculations on the allocated blocks based on the WebGL protocol, return the calculation results to the main GPU node, synthesize the terminal display image and display it.

[0104] Figure 2The described WebGL-based cloud collaborative 3D building modeling and rendering system can execute Figure 1 A WebGL-based cloud collaborative 3D building modeling and rendering method described in the illustrated embodiment, the implementation principle and technical effects will not be elaborated. For the WebGL-based cloud collaborative 3D building modeling and rendering system in the above embodiment, the specific ways for each module and unit to execute operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0105] In a possible design, Figure 2 A WebGL-based cloud collaborative 3D building modeling and rendering system described in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0106] The processing component 32 is used for the above Figure 1 A WebGL-based cloud collaborative 3D building modeling and rendering method described in the embodiment.

[0107] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0108] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0109] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0110] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0111] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0112] Wherein, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server. The above processing component, storage component, etc. can be basic server resources rented or purchased from a cloud computing platform.

[0113] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 shown embodiment of a WebGL-based cloud collaborative three-dimensional building modeling and rendering method.

[0114] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described system, system, and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0115] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0116] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A cloud-based collaborative 3D architectural modeling and rendering method based on WebGL, characterized in that: include: Obtain design parameters of the target building from a cloud database, parse the design parameters into geometric topological data of multiple independent components based on functional attribute division rules of building components, and dynamically generate a multi-level detailed geometric model of each independent component according to the terminal display resolution and viewpoint distance; Adding material physical properties to the surface vertex data of the multi-level detailed geometric model, calculating the diffuse reflection coefficient and the specular reflection intensity of the material physical properties and the lighting conditions of the building scene through a ray tracing algorithm, and generating lightweight component data carrying physical rendering parameters; In response to the perspective switching instruction of the terminal user, a main GPU node is allocated from the distributed GPU cluster according to the spatial block corresponding to the current perspective. The main GPU node divides the lightweight component data into spatial blocks, and allocates adjacent blocks of the spatial blocks to edge GPU nodes. The edge GPU nodes perform parallel ray tracing calculations on the allocated blocks based on the WebGL protocol, return the calculation results to the main GPU node, synthesize the terminal display image and display it.

2. The method according to claim 1, characterized in that: Also includes: When the multi-user collaborative editing operation triggers a change in the geometric shape or material property of at least one independent component, extracting the three-dimensional space bounding box range of the changed component; Based on the updated vertex coordinate values ​​and the changed material property values ​​within the three-dimensional space bounding box, the diffuse reflection coefficient and the specular reflection intensity of the changed component under the lighting direction of the building scene are recalculated to generate updated lightweight component data carrying physical rendering parameters; According to the corresponding spatial block of the three-dimensional space bounding box range, the updated lightweight component data is distributed to the distributed GPU cluster, the edge GPU node bound to the corresponding spatial block is triggered to perform parallel ray tracing secondary calculation, and the secondary calculation result is returned to the main GPU node for local frame synthesis to generate an updated rendering block; The updated rendering block is compared with the original display data of the unchanged area at the pixel level, the difference pixel coordinates and color values ​​are extracted, and the original display data is overwritten by the difference data stream.

3. The method according to claim 1, characterized in that The method of adding material physical properties to the surface vertex data of the multi-level detailed geometric model, calculating the diffuse reflection coefficient and the specular reflection intensity of the material physical properties and the lighting conditions of the building scene through a ray tracing algorithm, and generating lightweight component data carrying physical rendering parameters includes: Matching the material type corresponding to the multi-level detailed geometric model in the building material database, extracting the surface roughness, refractive index and metallicity parameters of the material type, and mapping them to the surface vertex data of the multi-level detailed geometric model; Based on the preset natural light direction and artificial light source position in the building scene, construct a light propagation path associated with the surface vertex data of the multi-level detailed geometric model, and calculate the first reflection direction and energy attenuation value of each light path at the surface vertex coordinates of the multi-level detailed geometric model according to the surface roughness and the refractive index; Dynamically adjusting the light sampling density of the surface vertex coordinates, wherein the number of secondary light sampling is increased for vertices in high curvature areas and the number of secondary light sampling is reduced for vertices in flat areas, to generate diffuse reflection coefficients and specular reflection intensity of the surface vertex coordinates; The diffuse reflection coefficient, the specular reflection intensity and the energy attenuation value are encapsulated as physical rendering parameters bound to the surface vertex coordinates, redundant vertex data not affected by illumination in the multi-level detail geometric model is eliminated, and lightweight component data carrying physical rendering parameters is generated.

4. The method according to claim 3, characterized in that The method comprises: constructing a light propagation path associated with the surface vertex data of the multi-level detail geometric model based on the preset natural light direction and artificial light source position in the architectural scene, and calculating the first reflection direction and energy attenuation value of each light path at the surface vertex coordinate according to the surface roughness and the refractive index, including: Reading the preset natural light direction angle and the three-dimensional space coordinates of the artificial light source from the building scene configuration file, and obtaining the radiation intensity and spectral distribution data of the artificial light source; Taking the surface vertex coordinates as the starting point, extending the light propagation path in the reverse direction along the natural light direction angle and the three-dimensional space coordinates, and establishing a direct illumination association relationship between the surface vertex coordinates and the artificial light source; Determine a microsurface normal perturbation range at the surface vertex coordinates according to the surface roughness, calculate a first reflection direction of an incident light ray at the surface vertex coordinates in combination with the refractive index, and randomly shift the first reflection direction based on the microsurface normal perturbation range; The energy attenuation value in the first reflection direction is calculated according to the radiation intensity of the artificial light source and the path length of the light propagation path.

5. The method according to claim 2, characterized in that: The triggering of the edge GPU node bound to the corresponding spatial block to perform parallel ray tracing secondary calculation, returning the secondary calculation result to the main GPU node for local frame synthesis, and generating an updated rendering block, includes: According to the boundary coordinates of the corresponding spatial block, the updated lightweight component data is divided into a plurality of rendering subtasks, and the rendering subtasks are distributed to edge GPU nodes bound to the corresponding spatial block; In the edge GPU node, a local light propagation path is constructed based on the geometric data and illumination parameters of the corresponding spatial block, and light energy distribution of the local light propagation path is calculated; Returning the light energy distribution to the main GPU node, performing local frame synthesis on the light energy distribution data according to the boundary coordinates, and generating local frame buffer data; Edge smoothing is performed on the local frame buffer data and the frame buffer data of the adjacent spatial block to generate an updated rendering block.

6. The method according to claim 3, characterized in that The step of encapsulating the diffuse reflection coefficient, the specular reflection intensity and the energy attenuation value into physical rendering parameters bound to the surface vertex coordinates, removing redundant vertex data not affected by illumination in the multi-level detail geometric model, and generating lightweight component data carrying physical rendering parameters includes: Generate a physical rendering parameter data block corresponding to the surface vertex coordinates according to the diffuse reflection coefficient, the specular reflection intensity and the numerical range of the energy attenuation value; Based on the illumination visibility detection result of the surface vertex coordinates, filter out coordinate data that is not affected by illumination, and mark the coordinate data that is not affected by illumination as redundant vertex data; Binding the physical rendering parameter data block to the surface vertex coordinates to generate a vertex data set carrying the physical rendering parameters; The redundant vertex data is eliminated from the multi-level detailed geometric model, the vertex data set is optimized according to the elimination result, and the lightweight component data carrying the physical rendering parameters is generated.

7. The method according to claim 1, characterized in that The functional attribute division rule based on the building components is used to parse the design parameters into geometric topological data of multiple independent components, and dynamically generate a multi-level detailed geometric model of each independent component according to the terminal display resolution and the viewpoint distance, including: According to the functional attribute division rules of building components, independent component data corresponding to walls, beams, columns, doors and windows are extracted from the design parameters to generate initial geometric topology data; Calculating a display detail level of the independent component data based on a terminal display resolution and a current viewpoint distance, and generating geometric simplification parameters matching the display detail level; According to the geometric simplification parameters, merging vertices and simplifying faces of the initial geometric topology data to generate optimized geometric topology data; The optimized geometric topology data is associated with the functional attribute division rule and stored, and a multi-level detailed geometric model of each independent component is dynamically generated.

8. A cloud-based collaborative 3D architectural modeling and rendering system based on WebGL, characterized in that: include: A parsing module is used to obtain the design parameters of the target building from the cloud database, parse the design parameters into geometric topological data of multiple independent components based on the functional attribute division rules of the building components, and dynamically generate a multi-level detailed geometric model of each independent component according to the terminal display resolution and the viewpoint distance; A calculation module is used to add material physical properties to the surface vertex data of the multi-level detailed geometric model, calculate the diffuse reflection coefficient and the specular reflection intensity of the material physical properties and the lighting conditions of the building scene through a ray tracing algorithm, and generate lightweight component data carrying physical rendering parameters; The synthesis module is used to respond to the perspective switching instruction of the terminal user, allocate a main GPU node from the distributed GPU cluster according to the spatial block corresponding to the current perspective, and the main GPU node divides the lightweight component data according to the spatial block, and allocates the adjacent blocks of the spatial block to the edge GPU node. The edge GPU node performs parallel ray tracing calculations on the allocated blocks based on the WebGL protocol, returns the calculation results to the main GPU node, and synthesizes the terminal display image and displays it.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a WebGL-based cloud-based collaborative three-dimensional architectural modeling and rendering method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a cloud-based collaborative three-dimensional architectural modeling and rendering method based on WebGL is implemented as described in any one of claims 1 to 7.

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