Efficient Culling Criteria for Continues Collision Detection Using a Fast Statistical Analysis Method
Fengquan Zhang*, Jiaojiao Guo, Jianfei Wan, Junli Qin
Identifiers and Pagination:Year: 2015
First Page: 569
Last Page: 573
Publisher Id: TOMEJ-9-569
Article History:Received Date: 17/2/2014
Revision Received Date: 21/3/2015
Acceptance Date: 9/6/2015
Electronic publication date: 10/9/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Continuous Collision Detection (CCD) between deforming triangle mesh elements in 3D is significant in many computer and graphics applications, such as virtual surgery, simulation and animation. Although CCD is more accurate than discrete methods, its application is limited mainly due to its time-consuming nature. To accelerate computation, we present an efficient CCD method to perform both inter-object and intra-object collision queries of triangle mesh models. Given a model set of different poses as training data, our method uses Statistic Analysis (SA) to make regression on a deformation subspace and also on collision detection conditions in a pre-processing stage, under a uniform framework. A data-driven training process selects a set of “key points” and produces a credible subspace representation, from which a plug-in type of collision culling certificate can be then obtained by regression process. At runtime, our certificate can be easily added to the classic BVH traversal procedure, as a sufficient condition of collision free cases, providing efficient culling in overlapping test and reducing hierarchy updates frequency. In the end, we describe performance and quality of our method using different experiments.