Triangle-Level Mesh Sampling for Dense 3D Fiber Placement
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Solution Overview
Problem
High-density point sampling techniques for fibrous structures in 3D graphical objects are inefficient, leading to delays and excessive resource utilization, particularly in interactive digital environments.
Innovation Solution
Implement triangle-level rejection sampling using a highly parallelized processor to generate a triangle-level sampling array, determining point samples and their locations efficiently across the mesh, reducing redundant calculations and memory allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-density point sampling technique is used to identify locations for fibrous structures, then the density and realism of fibrous surfaces is improved, but the computing resources and processing time are excessively consumed
Solution Approach 1:
The patent segments the mesh into triangular faces and performs rejection sampling at the triangle level rather than globally. Each triangle is processed independently with its own bounding box and sampling parameters, allowing parallelization and reducing redundant calculations across the entire mesh surface.
Solution Approach 2:
The patent uses rejection sampling that generates more candidate points than ultimately needed, then filters them based on acceptance criteria. This partial action approach (generating excess points then selecting) simplifies the sampling logic while maintaining high density where needed, trading some computational overhead for algorithmic simplicity and parallelizability.
2Measurement precision
If high-density point sampling technique is applied to generate fur on a 3D graphical object, then the realism of fibrous structures is improved, but the processing time and computational load increase to billions of calculations
Solution Approach 1:
The mesh is divided into triangular faces, each processed independently for point sample generation. This segmentation enables parallel processing where multiple triangles can be evaluated simultaneously, dramatically reducing total processing time while maintaining high fiber placement accuracy on each triangle surface.
Solution Approach 2:
The patent pre-calculates triangle properties (area, normals, bounding boxes) and organizes them in data structures before performing the actual sampling. This preliminary preparation reduces the computational work needed during the sampling phase, allowing faster generation of high-density point samples for fiber placement.
3Measurement precision
If high-density point sampling technique is used to identify point sample locations, then the quality of fibrous surface representation is improved, but the computing resources utilization becomes inefficient
Solution Approach 1:
Processing is segmented to the triangle level with independent bounding boxes and sampling parameters for each triangle. This allows the system to focus computational resources only on triangles that contribute to visible surfaces, avoiding waste on hidden or redundant areas while maintaining high surface coverage accuracy.
Solution Approach 2:
The patent implements dynamic culling where triangles are evaluated based on visibility, orientation, and other criteria during rendering. Triangles that are back-facing or occluded can be skipped entirely, dynamically adjusting resource consumption based on the actual viewing conditions while maintaining accurate fiber representation on visible surfaces.
Data Source
AI summary
A graphics generation computing device applies triangle-level rejection sampling to generate a set of surface mesh point samples. A highly parallelized processor included in the graphics generation computing device generates a triangle-level sampling array that includes triangle-level sampling data for each triangle included in a 3D object mesh. Based on the data in the triangle-level sampling array, the highly parallelized processor determines a quantity of point samples in each triangle. The highly parallelized processor calculates, for each point sample, point sample location data that indicates a location of the point sample on a triangle. The highly parallelized processor modifies a set of point samples to include the location data. In some cases, the set of point samples is used to generate digital fibers or other structure data objects at the point sample locations indicated by the set of point samples.