Accurate Self-Collision Detection Using Enhanced Dual-Cone Method

by Tongtong Wang1,Min Tang1,2 ,Zhendong Wang1, Ruofeng Tong1

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.
 

Contents

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},
  }

 

Related Links

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

Interactive Continuous Collision Detection between Deformable Models using Connectivity-Based Culling

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

Collision Detection

UNC GAMMA Group

Acknowledgements

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.

 


tang_m@zju.edu.cn