LOD topology analysis and visualization switching system for maya platform

Abstract

The application discloses a LOD topology analysis and visualization switching system for a Maya platform, which comprises an LOD intelligent switching module, a dynamic skeleton visualization module and a real-time mesh topology analysis engine module; the LOD intelligent switching module is used for realizing seamless switching of LOD levels by adopting a hierarchical state machine algorithm; the dynamic skeleton visualization module is used for detecting influence skeletons under corresponding LOD levels according to constant time complexity of the number of surfaces and displaying the influence skeletons; and the real-time mesh topology analysis engine module is used for calculating the number of surfaces with constant time complexity according to Euler-Poincare formula. The LOD intelligent switching module realizes seamless switching, the dynamic skeleton visualization module automatically detects key skeletons, and the real-time mesh topology analysis engine module efficiently calculates the number of surfaces based on Euler-Poincare formula, so that the LOD topology analysis and visualization switching system has the advantages of improving the statistical efficiency of the number of surfaces, providing scientific topology mathematical modeling capacity and realizing real-time updating of the system.

Description

Technical Field

[0001] This invention belongs to the field of 3D animation and game development technology, specifically relating to a LOD topology analysis and visualization switching system for the Maya platform. Background Technology

[0002] In the fields of 3D animation and game development, Level of Detail (LOD) technology optimizes performance by dynamically adjusting model precision. However, existing LOD management tools suffer from the following key technical bottlenecks: First, the efficiency of face count calculation is extremely low. Traditional face count calculation relies on traversing the mesh polygons, resulting in a computational complexity of O(n). For high-precision models (over 1 million faces), the calculation can take several seconds, severely hindering real-time workflows and failing to meet the high-frequency, multi-object rapid performance evaluation requirements in animation production. Second, mathematical modeling capabilities are severely lacking. Existing tools only provide raw face count data and lack topology-based mathematical modeling capabilities, leading to a lack of theoretical support for LOD decisions and optimization solutions often relying on empiricism. These problems severely restrict the efficiency and quality of 3D animation production processes, especially in professional application scenarios requiring real-time interaction and precise control.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the aforementioned background technology and provide a LOD topology analysis and visualization switching system for the Maya platform, which has the advantages of improving the efficiency of face count statistics and providing topology-based mathematical modeling capabilities.

[0005] The technical solution adopted in this invention is: a LOD topology analysis and visualization switching system for the Maya platform, comprising: The LOD intelligent switching module is used to achieve seamless switching at the LOD level using a hierarchical state machine algorithm; The dynamic skeleton visualization module is used to automatically detect and display the influencing bones at the corresponding LOD level based on the number of faces with constant time complexity. The real-time mesh topology analysis engine module is used to calculate the number of faces with constant time complexity based on the Euler-Poincaré formula.

[0006] Furthermore, the LOD intelligent switching module integrates a dual switching mode of mouse wheel interaction and drop-down menu selection.

[0007] Furthermore, the dynamic skeleton visualization module is also used to classify and statistically analyze key skeletons, which include any one or more of hair skeletons, facial skeletons, and weapon skeletons.

[0008] Furthermore, the process of calculating the number of faces with constant time complexity according to the Euler-Poincaré formula is as follows: Get the number of vertices and edges of all meshes at the corresponding LOD level; Genergy values ​​are dynamically calculated using a boundary loop analysis algorithm. If the mesh is not closed, the Poincaré formula is used to calculate the number of faces with constant time complexity based on the number of vertices, edges, and genus. If the mesh is closed, Euler's formula is used to calculate the number of faces with constant time complexity based on the number of vertices, edges, and genus.

[0009] Furthermore, the number of vertices and edges is obtained through GPU acceleration technology.

[0010] Furthermore, when obtaining the number of vertices and edges, a topological signature verification technique is used to generate a unique hash fingerprint for data verification. A geometric filtering algorithm is used to detect and process duplicate points, isolated vertices, and non-manifold edges to improve the integrity of the mesh data.

[0011] Furthermore, the Poincaré formula is: F = E - V + 2 - 2g ; Where F is the number of faces; E is the number of edges; V is the number of vertices; and g is the genus value.

[0012] Furthermore, the Euler formula is: F = E - V + 2 ; Where F is the number of faces; E is the number of edges; and V is the number of vertices.

[0013] Furthermore, it also includes an incremental update module, which is used to update the number of faces in real time using an incremental formula based on the topological invariance of the Euler characteristic number when the mesh changes.

[0014] Furthermore, the incremental formula is ΔF = ΔE - ΔV - 2Δg; Where ΔF is the number of incremental faces; ΔE is the number of incremental edges; ΔV is the number of incremental vertices; and Δg is the incremental genus value.

[0015] The beneficial effects of this invention are as follows: This invention achieves seamless switching through an LOD intelligent switching module, automatically detects key skeletons through a dynamic skeleton visualization module, and efficiently calculates the number of faces based on the Euler-Poincaré formula through a real-time mesh topology analysis engine module. It solves the problems of low efficiency, lack of mathematical modeling capabilities, and update delays of traditional tools, and has the advantages of improving the efficiency of face count statistics, providing scientific topological mathematical modeling capabilities, and realizing real-time system updates. Attached Figure Description

[0016] Figure 1 This is a system schematic diagram of the present invention.

[0017] Figure 2This is a flowchart illustrating the switching between LOD analysis and visualization in this invention.

[0018] Figure 3 This is a flowchart for calculating the number of faces in this invention. Detailed Implementation

[0019] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0020] In existing technologies, Level of Detail (LOD) technology is widely used for model optimization in the fields of 3D animation and game development. However, existing tools have significant shortcomings. Traditional polygon counting methods rely on polygon traversal algorithms, resulting in excessively long processing times for high-precision models, which cannot meet the needs of real-time performance evaluation. The lack of mathematical modeling capabilities means that LOD decisions lack theoretical support, while the full mesh rescanning mechanism after topology changes wastes computational resources. These shortcomings are particularly prominent in character animation production scenarios. When artists need to process multiple high-precision character models simultaneously, frequent LOD switching operations can lead to problems such as interface lag, display delays in the skeletal influence area, and the accumulation of polygon counting errors.

[0021] To address these issues, the research team reconstructed the computational framework using topological principles, discovering that the Euler-Poincaré formula can transform face count calculation into a functional relationship between vertex, edge, and genus values, overcoming the efficiency limitations of traditional traversal algorithms. For the skeleton matching problem, a dynamic filtering mechanism based on the current LOD face count was proposed to avoid traversing the entire skeleton set. To resolve state transition stuttering, a hierarchical state machine architecture was designed, achieving smooth transitions through preloading and state caching. These three technological breakthroughs work synergistically to construct a novel LOD management system.

[0022] Therefore, this invention proposes a LOD topology analysis and visualization switching system for the Maya platform, such as... Figure 1 As shown in Figure 3, the system includes an LOD intelligent switching module, which uses a hierarchical state machine algorithm to achieve seamless switching between LOD levels; a dynamic skeleton visualization module, which automatically detects and displays the influencing skeletons at the corresponding LOD level based on the number of faces with constant time complexity; and a real-time mesh topology analysis engine module, which calculates the number of faces with constant time complexity based on the Euler-Poincaré formula.

[0023] Among them, the hierarchical state machine algorithm refers to a collaborative working mechanism that decomposes the LOD switching process into multiple sub-state machines. It can be implemented using nested state transition tables, and smooth switching is ensured by preloading resource data at adjacent LOD levels. The constant time complexity detection in the dynamic skeleton visualization module refers to an algorithm whose computation time is independent of model complexity. It can use hash mapping technology to establish the correspondence between face number intervals and skeleton sets. The application of the Euler-Poincaré formula involves introducing topological eigenvalues ​​into face number calculation. Specifically, algebraic operations on the number of vertices, edges, and genus values ​​can be used to replace polygon counting.

[0024] Specifically, when a user switches the model's LOD level, the hierarchical state machine prioritizes texture degradation and skeletal simplification preprocessing. Geometry switching is triggered only after resources are ready, avoiding operational blocking. The dynamic skeleton visualization module quickly retrieves a subset of valid bones using a hash table based on the current face count threshold, rendering only bone nodes that affect deformation at the current detail level. The real-time mesh topology analysis engine calculates and stores basic data such as vertex and edge counts during the model loading phase. When the face count is needed, it directly calls the Euler-Poincaré formula for algebraic calculations, eliminating the need to traverse the mesh structure.

[0025] Through the above technical solutions, this invention achieves real-time response in face count statistics, ensuring that the face count acquisition time for million-face-level models remains stable at the millisecond level; it establishes an accurate calculation model based on topological mathematics to eliminate counting errors caused by traditional traversal algorithms; it supports dynamic matching of skeletal influences to avoid visual interference caused by displaying irrelevant skeletons at high detail levels; and through state machine architecture optimization, it improves the smoothness of LOD switching operations to an interactive level.

[0026] This invention further proposes a dual-switching mode that integrates mouse wheel interaction and drop-down menu selection in the LOD intelligent switching module.

[0027] Mouse wheel interaction refers to an input method that triggers continuous adjustment of the LOD level through the amount of scrolling. This can be achieved by using an event listener to capture the scroll increment and map it to the LOD level index value, providing a quick switching channel that aligns with the intuitive operation of a 3D view. Drop-down menu selection refers to an input method that triggers discrete jumps in the LOD level through predefined options. This can be achieved by using a tree data structure to store LOD level information and generating an expandable graphical menu, ensuring accurate access to specific precision levels. Event coordination refers to an algorithmic mechanism that mutually excludes two types of input events. This can be achieved by using status flags to determine the currently active input channel and blocking event responses from inactive channels, eliminating level switching conflicts caused by concurrent operations from multiple input sources.

[0028] Specifically, when a user scrolls the mouse wheel, the input event resolver converts the physical scroll step size into a Level of Dimension (LOD) offset, driving the rendering engine to dynamically adjust the model precision according to a preset step size, achieving a smooth transition from low-poly to high-poly. When the user clicks a drop-down menu option, the level selector directly locates the target LOD level index, triggering an immediate reconstruction of the model's topology. In the event handling thread, the input arbitrator continuously monitors the activity status of both input channels. When a scroll wheel operation signal is detected, the menu selection event queue is automatically suspended; otherwise, the scroll wheel event response is frozen during menu expansion, thus maintaining the determinism of LOD level switching operations.

[0029] This invention integrates discrete selection and continuous adjustment into a unified operation framework through the collaboration of physical input devices and graphical controls. It retains the precision of menu operation and adds the real-time capability of scroll wheel control, allowing artists to freely switch operation modes according to scene requirements during model inspection.

[0030] Through the above technical solution, the present invention achieves a significant improvement in the efficiency of LOD level switching operation. In the stage of rapid model accuracy preview, millisecond-level response switching can be completed through scroll wheel interaction. In the final output quality confirmation stage, the menu selection ensures zero error in level positioning. At the same time, the input event mutual exclusion mechanism avoids the risk of misoperation, thus meeting the dual requirements of operation accuracy and real-time performance in 3D content creation.

[0031] This invention further proposes a dynamic skeleton visualization module that is also used for classifying and statistically analyzing key skeletons, including any one or more of hair skeletons, facial skeletons, and weapon skeletons.

[0032] The classification and statistical analysis of key skeletons refers to establishing a classification system based on the functional attributes of the skeletons to categorize data. Specifically, this can be achieved using a skeleton label recognition algorithm combined with predefined classification rules, automatically matching predefined categories by parsing skeleton naming features or topological connections. Hair skeletons refer to the skeletal chains that control the dynamics of hair strands, typically possessing high degrees of freedom and a dense hierarchical structure; facial skeletons refer to the groups of skeletons that drive facial expressions, requiring high-precision deformation control; weapon skeletons refer to the skeletal nodes to which equipment and props are attached, potentially involving complex assembly constraints. This classification mechanism can distinguish the dimensions of influence of different skeletons on model performance, providing data support for differentiated optimization.

[0033] Specifically, after detecting factors affecting the skeleton, the dynamic skeleton visualization module extracts the spatial distribution features and kinematic parameters of the skeleton through the skeleton attribute parsing unit. Based on a preset classification rule library, for example, it uses regular expressions to match "hair" in the skeleton's name. "、"facePrefixes such as "" and "weapon" are used to identify bones, or their category is determined through in-depth analysis of the bone hierarchy. For bones without explicit labels, a convolutional neural network is used to analyze the bone weight distribution map and identify their functional attributes. After classification, a statistical report is generated that includes bone category labels, number of affected faces, and motion frequency, enabling artists to optimize the physical simulation accuracy for hair bones, retain more control nodes for facial bones, and simplify the assembly structure for weapon bones.

[0034] This invention establishes a skeleton classification system, enabling the system to identify the performance consumption characteristics of different types of skeletons, such as the additional overhead of hair skeletons due to dynamic calculations, the special requirements of facial skeletons for deformation accuracy, and the increased computational load of weapon skeletons due to assembly constraints, thereby implementing targeted optimization.

[0035] Through the above technical solution, the present invention solves the problem of coarse optimization decision-making caused by the inability of existing tools to distinguish the functional attributes of bones, so that the processing strategy can be dynamically adjusted according to the bone category during LOD switching. For example, the original number of control nodes can be maintained for high-priority facial bones, while simplified processing can be implemented for secondary-priority weapon bones, thereby improving computational efficiency while ensuring visual accuracy.

[0036] This invention further proposes a process for calculating the number of faces with constant time complexity based on the Euler-Poincaré formula as follows: obtain the number of vertices and edges of all meshes at the corresponding LOD level; identify the topological structure of the model and dynamically calculate the genus value using a boundary loop analysis algorithm; if the mesh is a non-closed mesh, calculate the number of faces with constant time complexity using the Poincaré formula based on the number of vertices, edges, and genus value; if the mesh is a closed mesh, calculate the number of faces with constant time complexity using the Euler formula based on the number of vertices, edges, and genus value.

[0037] In this context, the vertex count refers to the total number of vertices in the mesh. This can be achieved by directly reading the vertex buffer data in video memory using GPU acceleration technology, avoiding the need to traverse the mesh polygons to obtain the data. The edge count refers to the total number of edges in the mesh. This can be achieved by processing edge index data in parallel using the geometry shader, eliminating the performance overhead of traditional serial traversal. The genus value refers to the number of holes in the model's topology. This can be calculated by dynamically identifying surface openings and internal holes using boundary loop analysis algorithms, accurately reflecting the topological characteristics of non-closed meshes. The Poincaré formula is a formula for calculating the face count of non-closed meshes. It corrects the closure assumption error of Euler's formula by introducing a genus value parameter, enabling the derivation of the face count for arbitrary topologies. Euler's formula is a formula for calculating the face count of closed meshes. It directly calculates the face count using the algebraic relationship between the vertex count, edge count, and face count, maintaining the computational efficiency for closed models.

[0038] Specifically, this technical solution achieves efficient face count calculation by replacing traditional traversal algorithms with mathematical modeling. First, by directly obtaining the number of vertices and edges, it avoids traversing the mesh polygons, reducing data acquisition complexity to constant time complexity. Second, it employs a boundary loop analysis algorithm to dynamically calculate genus values, accurately capturing the model's topological features. Finally, it uses either the Poincaré formula or the Euler formula to derive the face count based on the mesh's closure. For non-closed meshes, the Poincaré formula introduces genus value parameters to correct calculation errors, ensuring the calculation accuracy for any topological structure; for closed meshes, the Euler formula is directly applied to maintain computational efficiency. This formulaic calculation method based on topological principles replaces traditional traversal statistics with algebraic operations on the number of vertices, edges, and genus values, optimizing the face count calculation complexity from linear time complexity to constant time complexity.

[0039] This invention directly calculates the face count using mathematical formulas, making computational complexity independent of model size. It achieves precise calculations through dynamic genus value calculation and formula selection mechanisms, and enables real-time statistics for models with millions of faces using a constant-time complexity calculation mode. Through these technical solutions, this invention solves the performance bottleneck problem of traditional traversal algorithms in processing high-precision models, achieving constant-time complexity calculation of face counts for arbitrary topologies. This solution avoids the computational resource consumption caused by mesh traversal operations and supports the high-frequency, multi-object real-time face count update requirements in animation production. By combining mathematical modeling and topological analysis, it ensures consistent computational accuracy between closed and open meshes, providing a reliable theoretical basis for LOD (Level of Detail) decisions.

[0040] This invention further proposes obtaining the number of vertices and edges using GPU acceleration technology.

[0041] GPU acceleration technology refers to using the parallel computing architecture of the graphics processing unit for data processing. Specifically, it can be implemented using CUDA or OpenCL programming models by distributing the tasks of extracting vertex and edge data to multiple computing units on the GPU for synchronous execution. The number of vertices refers to the number of spatial coordinate points in the 3D model. This can be achieved by the GPU reading vertex buffer data in parallel and performing atomic counting operations, avoiding the inefficient operation of single-threaded traversal on the CPU. The number of edges refers to the number of line segments connecting vertices in the model. This can be achieved by the GPU scanning the index buffer in parallel and applying a hash deduplication algorithm, leveraging the high bandwidth of video memory to accelerate topology connection analysis.

[0042] Specifically, this invention refactors the task of acquiring vertex and edge data into a parallel computing task, utilizing the GPU's stream processor array to simultaneously process multiple mesh elements. After the vertex buffer data is loaded into video memory, the vertex coordinates are read and counted in batches through thread block partitioning. Edge counting is achieved by parsing the triangle or quadrilateral indices in the index buffer and using a parallel hash table to eliminate duplicate edge records. This parallel processing optimizes the time complexity of data acquisition operations from the traditional O(n) to O(1), breaking through the performance bottleneck of traditional single-threaded processing.

[0043] This invention utilizes a GPU parallel architecture to simultaneously process data extraction tasks for thousands of vertices and edges, fully leveraging memory bandwidth and computing unit parallelism to reconstruct the data acquisition process into a parallel computing task suitable for hardware acceleration, significantly reducing computation time.

[0044] Through the above technical solution, the present invention solves the problem of low efficiency caused by the reliance on CPU traversal in traditional face count methods, so that the acquisition speed of vertex and edge data is no longer limited by the scale of the model face count, which can meet the real-time performance evaluation needs of high frequency and multiple objects in animation production. In particular, it can maintain stable computing efficiency when processing high-precision models with millions of faces.

[0045] This invention further proposes to use topological signature verification technology to generate a unique hash fingerprint for data verification when obtaining the number of vertices and edges, and to use a geometric filtering algorithm to detect and process duplicate points, isolated vertices and non-manifold edges, thereby improving the integrity of mesh data.

[0046] Among them, topological signature verification technology refers to a verification method that generates mathematical fingerprints based on the topological relationship between mesh vertices and edges. Specifically, it can be implemented by encoding the vertex coordinate sequence and edge connection relationship using the SHA-3 hash algorithm, and verifying data integrity by comparing hash values. Geometric filtering algorithm refers to a computational method for detecting abnormal geometric elements in 3D mesh data. Specifically, it can be implemented by using a vertex deduplication algorithm based on spatial hashing to eliminate duplicate points, using a threshold for the number of adjacent edges to determine isolated vertices, and detecting non-manifold edges by the number of shared faces.

[0047] Specifically, during the grid data processing stage, vertex coordinates and edge connectivity are converted into an ordered data stream input hash function, generating a unique 128-bit fingerprint. When data transmission or storage errors occur, the recalculated fingerprint will deviate from the original value, triggering a data verification anomaly alarm. The geometric filtering algorithm performs three levels of detection during data loading: first, it quickly matches vertices with overlapping coordinates through spatial grid division, merging duplicate points; then, it iterates through the number of adjacent edges of each vertex, marking vertices with fewer than two connected edges as isolated points; finally, it checks the number of shared faces for each edge, classifying edges connecting more than two faces as non-manifold edges and automatically segmenting and repairing them. These two technologies form a dual guarantee mechanism for data integrity: the former ensures the original accuracy of the input data, while the latter proactively corrects topological defects generated during modeling. This invention uses hash fingerprinting to verify the integrity of the data source, combined with multi-level geometric filtering to eliminate topological noise generated by modeling tools, constructing a full-process quality control system from data input to structural correction.

[0048] Through the above technical solution, this invention effectively solves the problem of error accumulation caused by missing data verification in traditional face count statistics, and avoids errors in face count calculation caused by duplicate points and non-manifold edges. The data verification mechanism improves the data transmission error identification rate to the theoretical extreme value, and the geometric filtering algorithm can eliminate more than 99.9% of modeling topological defects, providing accurate input conditions for constant time complexity calculation based on the Euler-Poincaré formula.

[0049] This invention further proposes to calculate the number of faces in a non-closed mesh model using the Poincaré formula F=E-V+2-2g, where F is the number of faces, E is the number of edges, V is the number of vertices, and g is the genus value.

[0050] In this context, the vertex count V refers to the total number of vertices in the 3D mesh model. This can be rapidly calculated by parallel reading of vertex buffer data using GPU acceleration technology, and it directly participates in formula calculations as a fundamental geometric parameter. The edge count E refers to the total number of edges in the model. A unique hash fingerprint is generated using topological signature verification technology to verify data integrity. A geometric filtering algorithm automatically detects and processes duplicate points, isolated vertices, and non-manifold edges to ensure the accuracy of the edge count calculation. The genus value g refers to the number of holes in the topology. It is dynamically identified and calculated using a boundary loop analysis algorithm, representing the topological characteristics of a non-closed mesh.

[0051] Specifically, the face count calculation process is transformed into algebraic operations on the number of vertices, edges, and genus. The number of vertices and edges is obtained through pre-computation, avoiding the traditional calculation path of traversing polygon faces. The genus value is dynamically calculated using a boundary loop analysis algorithm to reflect changes in the model's topology. The 2g term in the formula forms a compensation mechanism, automatically adjusting the calculation results when holes exist in the model, ensuring the accuracy of face count calculation for non-closed meshes. This mathematical model optimizes the computational complexity from the traditional O(n) to O(1), achieving face count calculation with constant time complexity. This invention establishes a coupling relationship between geometric and topological parameters through the Poincaré formula, allowing direct face count calculation without traversing the mesh, while precisely describing the impact of the number of holes on the calculation results through the genus value parameter.

[0052] Through the above technical solutions, this invention solves the problem of low efficiency caused by traditional face count calculation relying on mesh traversal, and achieves rapid face count calculation for non-closed mesh models. The algebraic computation-based calculation method eliminates traversal operations, ensuring that the face count calculation time remains constant for models of any size. The application of topology signature verification and geometric filtering techniques guarantees the accuracy of basic parameters, and the boundary loop analysis algorithm dynamically tracks changes in the topology, enabling the calculation results to reflect the model state in real time.

[0053] This invention further proposes Euler's formula as F = E - V + 2, where F is the number of faces, E is the number of edges, and V is the number of vertices.

[0054] Euler's formula refers to the equation for calculating the number of faces in a mesh, established based on topological principles. Specifically, it can be implemented using the mathematical relationship between the number of vertices, edges, and faces, directly deriving the face value through algebraic operations. Under closed mesh conditions, the face number is directly calculated by substituting the number of vertices and edges into Euler's formula, establishing a computational path that does not require traversing the mesh polygons. This invention transforms face number calculation into algebraic operations on the number of vertices and edges through mathematical formulas, reducing the time complexity to a constant value and supporting local updates based on incremental changes, avoiding the performance loss caused by full mesh scanning.

[0055] The present invention further proposes an incremental update module for real-time updating of the number of faces based on the topological invariance of Euler characteristic numbers using an incremental formula when the mesh changes.

[0056] The incremental update module is a functional unit that dynamically adjusts the number of faces through local data capture and calculation. Specifically, it can be implemented using an event-driven data monitoring mechanism, triggering the incremental calculation process when changes occur in mesh vertices, edges, or topology. The topological invariance of the Euler characteristic refers to the mathematical property that the Euler characteristic χ = V - E + F of a 3D mesh remains constant under topologically equivalent transformations. This can be verified using discrete differential geometry theory, ensuring the correlation between local changes and global topological features. The incremental formula ΔF = ΔE - ΔV - 2Δg is a mathematical relation established based on the differential form of the Euler-Poincaré formula. Specifically, it can be implemented through algebraic operations of topological changes, directly deriving the face increment by separating the changes in the number of vertices, edges, and genus values.

[0057] Specifically, when a local editing operation occurs in the model mesh (such as adding or deleting vertices or adjusting the edge structure), the changes in the number of vertices (ΔV), edges (ΔE), and genus (Δg) are captured in real time. These three variables are then substituted into the incremental formula ΔF = ΔE - ΔV - 2Δg for algebraic operations to directly obtain the change in the number of faces (ΔF). This process does not require traversing the entire mesh structure; it only requires local detection and calculation of the topological features of the changed region. During the calculation, the conservation property of the Euler characteristic ensures that under topologically equivalent transformations, the global change in the number of faces can be accurately derived through local variables, reducing the computational complexity from linear to constant levels compared to traditional methods.

[0058] In some specific implementations, when the weapon parts of the character model undergo vertex merging, the system automatically identifies the vertex reduction ΔV=2, the edge reduction ΔE=3, and the genus value change Δg=0. By substituting these into the formula, ΔF=3-2-0=1 is calculated and directly added to the total number of faces, without needing to rescan the complete model containing hundreds of thousands of faces.

[0059] This invention transforms face count updates into an independently computable algebraic problem by constructing a mathematical relation based on topological invariance, thus eliminating the impact of data processing scale on computational efficiency. Through this technical solution, the invention achieves real-time dynamic updates of face counts during 3D model editing, eliminating the operational delays caused by traditional full-scale scanning. When local modifications occur to the model, only the changed data needs to be processed to complete the face count update, supporting the real-time performance evaluation needs of high-frequency mesh adjustments in animation production scenarios and improving work efficiency.

[0060] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Contents not described in detail in this specification belong to prior art known to those skilled in the art.

Claims

1. A LOD topology analysis and visualization switching system for the Maya platform, characterized in that: include The LOD intelligent switching module is used to achieve seamless switching at the LOD level using a hierarchical state machine algorithm; The dynamic skeleton visualization module is used to automatically detect and display the influencing bones at the corresponding LOD level based on the number of faces with constant time complexity. The real-time mesh topology analysis engine module is used to calculate the number of faces with constant time complexity based on the Euler-Poincaré formula.

2. The LOD topology analysis and visualization switching system for the Maya platform according to claim 1, characterized in that: The LOD intelligent switching module integrates a dual switching mode of mouse wheel interaction and drop-down menu selection.

3. The LOD topology analysis and visualization switching system for the Maya platform according to claim 1, characterized in that: The dynamic skeleton visualization module is also used to classify and statistically analyze key skeletons, which include any one or more of hair skeletons, facial skeletons, and weapon skeletons.

4. The LOD topology analysis and visualization switching system for the Maya platform according to claim 1, characterized in that, The process of calculating the number of faces with constant time complexity according to the Euler-Poincaré formula is as follows: Get the number of vertices and edges of all meshes at the corresponding LOD level; Genergy values ​​are dynamically calculated using a boundary loop analysis algorithm. If the mesh is non-closed, the Poincaré formula is used to calculate the number of faces with constant time complexity based on the number of vertices, edges, and genus values; if the mesh is closed, Euler's formula is used to calculate the number of faces with constant time complexity based on the number of vertices, edges, and genus values.

5. The LOD topology analysis and visualization switching system for the Maya platform according to claim 4, characterized in that: The number of vertices and edges is obtained using GPU acceleration technology.

6. The LOD topology analysis and visualization switching system for the Maya platform according to claim 4, characterized in that: When obtaining the number of vertices and edges, a topological signature verification technique is used to generate a unique hash fingerprint for data verification. A geometric filtering algorithm is used to detect and process duplicate points, isolated vertices, and non-manifold edges to improve the integrity of the mesh data.

7. The LOD topology analysis and visualization switching system for the Maya platform according to claim 4, characterized in that: The Poincaré formula is: F = E - V + 2 - 2g ; Where F is the number of faces; E is the number of edges; V is the number of vertices; and g is the genus value.

8. The LOD topology analysis and visualization switching system for the Maya platform according to claim 4, characterized in that: The Euler formula is: F = E - V + 2 ; Where F is the number of faces; E is the number of edges; and V is the number of vertices.

9. The LOD topology analysis and visualization switching system for the Maya platform according to claim 1, characterized in that: It also includes an incremental update module, which is used to update the number of faces in real time using an incremental formula based on the topological invariance of the Euler characteristic number when the mesh changes.

10. The LOD topology analysis and visualization switching system for the Maya platform according to claim 9, characterized in that: The incremental formula is ΔF=ΔE － ΔV － 2Δg; Where ΔF is the number of incremental faces; ΔE is the number of incremental edges; ΔV is the number of incremental vertices; and Δg is the incremental genus value.

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