Efficient and Reliable Self‐Collision Culling Using Unprojected Normal Cones
Abstract
We present an efficient and accurate algorithm for self‐collision detection in deformable models. Our approach can perform discrete and continuous collision queries on triangulated meshes. We present a simple and linear time algorithm to perform the normal cone test using the unprojected 3D vertices, which reduces to a sequence point‐plane classification tests. Moreover, we present a hierarchical traversal scheme that can significantly reduce the number of normal cone tests and the memory overhead using front‐based normal cone culling. The overall algorithm can reliably detect all (self) collisions in models composed of hundreds of thousands of triangles. We observe considerable performance improvement over prior continuous collision detection algorithms.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 20, 2017
- Source ID
- 10.1111/cgf.13095
Entities
People
- Dinesh Manocha
- Min Tang
- Ruofeng Tong
- Tongtong Wang
- Zhihua Liu
Organizations
- Army Research Office
- Ministry of Education of the People's Republic of China
- National Natural Science Foundation of China
- University of North Carolina at Chapel Hill
- Zhejiang Provincial Natural Science Foundation
- Zhejiang University