Efficient and Reliable Self‐Collision Culling Using Unprojected Normal Cones
| dc.contributor.author | Wang, Tongtong | en_US |
| dc.contributor.author | Liu, Zhihua | en_US |
| dc.contributor.author | Tang, Min | en_US |
| dc.contributor.author | Tong, Ruofeng | en_US |
| dc.contributor.author | Manocha, Dinesh | en_US |
| dc.contributor.editor | Chen, Min and Zhang, Hao (Richard) | en_US |
| dc.date.accessioned | 2018-01-10T07:43:13Z | |
| dc.date.available | 2018-01-10T07:43:13Z | |
| dc.date.issued | 2017 | |
| dc.description.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.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. | en_US |
| dc.description.number | 8 | |
| dc.description.sectionheaders | Articles | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 36 | |
| dc.identifier.doi | 10.1111/cgf.13095 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 487-498 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13095 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13095 | |
| dc.publisher | © 2017 The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | collision detection | |
| dc.subject | animation | |
| dc.title | Efficient and Reliable Self‐Collision Culling Using Unprojected Normal Cones | en_US |