标题:Mobility Fitting using 4D RANSAC
作者:Li, Hao; Wan, Guowei; Li, Honghua; Sharf, Andrei; Xu, Kai; Chen, Baoquan
作者机构:[Li, Hao; Wan, Guowei; Li, Honghua; Chen, Baoquan] Shandong Univ, Jinan, Peoples R China.; [Sharf, Andrei] Ben Gurion Univ Negev, Beer Sheva, Israel 更多
会议名称:Eurographics Symposium on Geometry Processing (SGP) / Symposium on Solid and Physical Modeling (SPM) / Shape Modeling International (SMI) Conference
会议日期:JUN 20-24, 2016
来源:COMPUTER GRAPHICS FORUM
出版年:2016
卷:35
期:5
页码:79-88
DOI:10.1111/cgf.12965
摘要:Capturing the dynamics of articulated models is becoming increasingly important. Dynamics, better than geometry, encode the functional information of articulated objects such as humans, robots and mechanics. Acquired dynamic data is noisy, sparse, and temporarily incoherent. The latter property is especially prominent for analysis of dynamics. Thus, processing scanned dynamic data is typically an ill-posed problem. We present an algorithm that robustly computes the joints representing the dynamics of a scanned articulated object. Our key idea is to by-pass the reconstruction of the underlying surface geometry and directly solve for motion joints. To cope with the often-times extremely incoherent scans, we propose a space-time fitting-and-voting approach in the spirit of RANSAC. We assume a restricted set of articulated motions defined by a set of joints which we fit to the 4D dynamic data and measure their fitting quality. Thus, we repeatedly select random subsets and fit with joints, searching for an optimal candidate set of mobility parameters. Without having to reconstruct surfaces as intermediate means, our approach gains the advantage of being robust and efficient. Results demonstrate the ability to reconstruct dynamics of various articulated objects consisting of a wide range of complex and compound motions.
收录类别:CPCI-S;EI;SCOPUS;SCIE
WOS核心被引频次:2
Scopus被引频次:3
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982124156&doi=10.1111%2fcgf.12965&partnerID=40&md5=fd3860fd37f4fffc798068b7d7b21407
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