Deformation similarity clustering based collision detection in clothing simulation

The 3D dynamic clothing simulation is widely used in computer-added garment design. Collision detection and response are the essential component and also the efficiency bottleneck in the simulation. The purpose of this paper is to propose a high efficient collision detection algorithm for 3D clothing-human dynamic simulation to achieve both real-time and virtually real simulation effects.

The authors approach utilizes the offline data learning results to simplify the online collision detection complexity. The approach includes two stages. In the off-line stage, model triangles with most similar deformations are first, partitioned into several near-rigid-clusters. Clusters from the clothing model and the human model are matched as pairs according to the fact that they hold the potential to intersect. For each cluster, a hierarchical bounding box tree is then constructed. In the on-line stage, collision detection is checked and treated parallelly inside each cluster pairs. A multiple task allocation strategy is proposed in parallel computation to ensure efficiency.

Reasonably partitioning the 3D clothing and human model surfaces into several clusters and implementing collision detection on these cluster pairs can efficiently reduce the model primitive amounts that need be detected, consequently both improving the detection efficiency and remaining the simulation virtual effect.

The current methods only utilize the dynamic clothing-human status; the authors algorithm furthermore combines the intrinsic correspondence relationship between clothing and human clusters to efficiently shrink the detection query scope to accelerate the detection speed. Moreover, partitioning the model into several independent clusters as detection units is much more profitable for parallel computation than current methods those treat the model entirety as the unit.