Selective Surface Mesh Regeneration for 3D Renderings
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Solution Overview
Problem
Generating surface meshes for 3D models of dynamic environments is computationally expensive, as existing methods regenerate the entire mesh upon updates, even if only minor changes occur, leading to inefficiency and potential reduction in model resolution.
Innovation Solution
A method that selectively regenerates the surface mesh only for subsets of the 3D voxel grid that exhibit greater than threshold changes in signed values over time, using a 3D voxel grid with signed distance function values and algorithms like Marching Cubes, to preserve computational resources while maintaining high resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire surface mesh is regenerated upon receiving new depth information, then the 3D model remains up-to-date with current environment changes, but computational resources and processing time are wasted on regions that have not changed
Solution Approach 1:
The patent divides the 3D voxel grid into multiple subsets or regions, allowing selective processing of only those subsets that exhibit significant changes in signed distance values. This segmentation enables the system to maintain model reliability by updating changed regions while avoiding computational waste on unchanged regions.
Solution Approach 2:
Instead of performing full mesh regeneration (excessive action), the patent applies partial action by regenerating mesh only for subsets where the change in signed distance values exceeds a threshold. This partial regeneration maintains sufficient model accuracy while significantly reducing computational resource consumption.
2Manufacturing precision
If the entire surface mesh is regenerated frequently to maintain high resolution, then the model quality is preserved, but computational power consumption increases
Solution Approach 1:
The patent applies different processing quality levels to different regions of the 3D model. Regions with significant changes (above threshold) receive full mesh regeneration to maintain high resolution, while regions without significant changes retain their existing mesh. This local quality approach preserves overall model resolution while reducing total computational power consumption.
Solution Approach 2:
The patent performs partial mesh regeneration only where necessary to maintain resolution, rather than regenerating the entire mesh. By threshold-based selection of subsets requiring updates, the system achieves acceptable model resolution with reduced computational power expenditure.
3Productivity
If selective regeneration is performed only on subsets with threshold changes, then computational efficiency improves, but processing time for determining which subsets to update increases
Solution Approach 1:
The patent replaces expensive full mesh regeneration operations with lighter threshold comparison operations. By substituting the heavy computational mechanics of full regeneration with simpler threshold-based subset identification, the system achieves net time savings despite the added evaluation step.
Solution Approach 2:
The patent performs preliminary threshold-based change detection on signed distance values before committing to full mesh regeneration. This preliminary action filters out subsets that don't require updates, preventing wasteful regeneration operations and reducing total processing time.
Data Source
AI summary
A 3D voxel grid of an environment is generated, with a signed value assigned to each voxel representative of that voxel's distance from a nearest surface. For each subset of the 3D voxel grid comprising a surface, an initial surface mesh is generated. As new depth information is received, an updated surface is generated only for those subsets of the 3D voxel grid that exhibit greater than threshold change in signed values over time.