Efficient and Reliable Self‐Collision Culling Using Unprojected Normal Cones. (20th March 2017)
- Record Type:
- Journal Article
- Title:
- Efficient and Reliable Self‐Collision Culling Using Unprojected Normal Cones. (20th March 2017)
- Main Title:
- Efficient and Reliable Self‐Collision Culling Using Unprojected Normal Cones
- Authors:
- Wang, Tongtong
Liu, Zhihua
Tang, Min
Tong, Ruofeng
Manocha, Dinesh - Abstract:
- 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. 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 improvementAbstract: 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. 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. … (more)
- Is Part Of:
- Computer graphics forum. Volume 36:Number 8(2017)
- Journal:
- Computer graphics forum
- Issue:
- Volume 36:Number 8(2017)
- Issue Display:
- Volume 36, Issue 8 (2017)
- Year:
- 2017
- Volume:
- 36
- Issue:
- 8
- Issue Sort Value:
- 2017-0036-0008-0000
- Page Start:
- 487
- Page End:
- 498
- Publication Date:
- 2017-03-20
- Subjects:
- collision detection -- animation
Computer graphics -- Periodicals
006.605 - Journal URLs:
- http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8659.1982.tb00001.x/abstract ↗
http://onlinelibrary.wiley.com/ ↗
http://www.blackwell-synergy.com/servlet/useragent?func=showIssues&code=cgf ↗ - DOI:
- 10.1111/cgf.13095 ↗
- Languages:
- English
- ISSNs:
- 0167-7055
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3393.982000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 5625.xml