Convex Hull Simplification with Vertex and Face Removal for Collision Accuracy
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Solution Overview
Problem
Existing methods for simplifying convex hulls in 3D graphics applications often result in artifacts or inefficiencies due to the reduction of vertex count, leading to poor collision detection and physics operations.
Innovation Solution
A method that iteratively mixes vertex removal and face removal operations, using a unified cost metric to balance vertex count reduction with maintaining a robust representation of the original 3D object, by sorting candidate vertices and faces based on ascending cost metrics to generate a simplified convex hull.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the vertex count of a convex hull is reduced to meet a vertex budget, then the computational complexity is reduced and processing speed is improved, but the accuracy of collision detection and physics operations deteriorates
Solution Approach 1:
The simplification process segments the convex hull modification into two distinct operations: vertex removal and face removal. By separating these operations and evaluating them independently through cost metrics, the system can selectively apply the most effective simplification strategies to balance vertex count reduction with geometric accuracy preservation.
Solution Approach 2:
The system changes the approach from purely vertex-based simplification to a hybrid approach that also removes faces. This parameter change in the simplification methodology allows for more effective reduction of vertex count while maintaining the convex hull's accuracy as a representation of the original 3D object.
2Device complexity
If only vertex removal operations are used to simplify the convex hull, then the implementation is simple, but the convex hull may shrink excessively and lose accuracy
Solution Approach 1:
The system merges two simplification operations (vertex removal and face removal) into a unified framework that uses a single cost metric to evaluate and select between them. This combination allows the system to leverage the advantages of both operations while avoiding their individual limitations through coordinated application.
Solution Approach 2:
The cost metric framework provides a universal evaluation mechanism that works for both vertex removal and face removal operations. This universal approach allows the system to adaptively choose the most appropriate simplification operation based on the current state of the convex hull and the desired balance between complexity reduction and accuracy preservation.
3Productivity
If the convex hull is simplified aggressively to reduce vertex count, then the computational efficiency improves, but artifacts are introduced in collision detection
Solution Approach 1:
The system uses cost metrics as feedback mechanisms to evaluate the impact of each simplification operation on the convex hull's accuracy. By continuously monitoring and using this feedback to guide the simplification process, the system can avoid introducing artifacts while still achieving the desired reduction in vertex count and computational complexity.
Data Source
AI summary
A method, device, and computer-readable storage medium for simplifying a convex hull are disclosed. A first queue of candidate vertices of a convex hull for vertex removal is generated, wherein the candidate vertices are sorted in the first queue by ascending values of a first cost metric associated with removal of the candidate vertex. A second queue of candidate faces of the convex hull for face removal is generated, wherein the candidate faces are sorted in the second queue by ascending values of a second cost metric associated with removal of the candidate face. A simplification operation is performed on the convex hull to generate a simplified version of the convex hull by performing a vertex removal operation on the candidate vertex in the first queue with lowest first cost metric or performing a face removal operation on the candidate face in the second queue with lowest second cost metric.