1 - Zhejiang University, China
2 - Alibaba-Zhejiang University Joint Institute of Frontier Technologies, China
Benchmarks: We use five challenging benchmarks involving deformable models and cloth simulations for performance comparisons between our DCD and CCD algorithms and previous methods. Our method can reliably detect all self-collisions on these models. However, the previous dual-cone method can generate false negatives for Pipe, T-shirt and Karate.
Abstract
We present an accurate and robust algorithm for self-collision detection in deformable models. Our method is based on the normal
cone test and is suitable for both discrete and continuous collision queries on triangular meshes. We propose a novel means of
employing surface normal cones and binormal cones to perform the normal cone test. Moreover, we combine our culling criteria
with bounding volume hierarchies (BVHs) and present a hierarchical traversal scheme. Unlike the previous BVH-based dual-cone
method, our method can reliably detect all self-collisions, and it achieves appreciable speedup over other high-level culling methods.
Paper (PDF 1.03 MB)
Tongtong Wang, Min Tang, Zhendong Wang, and Roufeng Tong, Accurate Self-Collision Detection Using Enhanced Dual-Cone Method, Computers & Graphics, 73, 70-79, 2018.
@article{Dcc18,
author = {Tongtong Wang, Min Tang, Zhendong Wang, and Roufeng Tong},
title = {{DCC}: Accurate Self-Collision Detection Using Enhanced Dual-Cone Method},
journal = {Computers & Graphics},
volume = {73},
pages = {70--79},
year = {2018},
}
I-Cloth: Incremental Collision Handling for GPU-Based Interactive Cloth Simulation
PSCC: Parallel Self-Collision Culling with Spatial Hashing on GPUs
I-Cloth: API for fast and reliable cloth simulation with CUDA
Efficient BVH-based Collision Detection Scheme with Ordering and Restructuring
CAMA: Contact-Aware Matrix Assembly with Unified Collision Handling for GPU-based Cloth Simulation
A GPU-based Streaming Algorithm for High-Resolution Cloth Simulation
UNC dynamic model benchmark repository
Collision-Streams: Fast GPU-based Collision Detection for Deformable Models
Fast Continuous Collision Detection using Deforming Non-Penetration Filters
MCCD: Multi-Core Collision Detection between Deformable Models using Front-Based Decomposition
Fast Collision Detection for Deformable Models using Representative-Triangles
DeformCD: Collision Detection between Deforming Objects
Self-CCD: Continuous Collision Detection for Deforming Objects
Interactive Collision Detection between Deformable Models using Chromatic Decomposition
Fast Proximity Computation Among Deformable Models using Discrete Voronoi Diagrams
CULLIDE: Interactive Collision Detection between Complex Models using Graphics Hardware
RCULLIDE: Fast and Reliable Collision Culling using Graphics Processors
Quick-CULLIDE: Efficient Inter- and Intra-Object Collision Culling using Graphics Hardware
This work was supported by the National Key R&D Program of China [2017YFB1002703]; NSFC [61732015, 61572423, 61572424]; the Science and Technology Project of Zhejiang Province [2018C01080]; and Zhejiang Provincial NSFC [LZ16F020003]. We also thank Zhihua Liu for useful discussions.