Kim, Youngji Lim, Hwasup Ahn, Sang Chul Kim, Ayoung 2024-01-19T11:09:15Z 2024-01-19T11:09:15Z 2022-03-01 2016-12-01 https://pubs.kist.re.kr/handle/201004/114686 In this paper, we present a unified approach for Expectation Maximization (EM) based motion segmentation, estimation and analysis from dense point cloud data. When identifying an underlying motion, literature mainly focuses on three related topics: motion segmentation, estimation and analysis. These topics are, however, mostly considered separately while integrated approaches are rare. Our approach specifically focuses on analyzing articulated motion from dense point cloud data by simultaneously solving for three topics using an integrated approach. No prior knowledge, such as background regions, number of segments and correspondence, is required since two iterations in this algorithm allow us to seamlessly accomplish integration of the three tasks. The first iteration of the algorithm is performed between segmentation and estimation, followed by the second iteration between motion estimation and analysis. For the first iteration, we propose EM based subspace clustering algorithm. For the second iteration, we simply fuse the motion analysis method from [1] into an iterative motion estimation algorithm. As a result, we can extract label, correspondence and motion of moving objects simultaneously from dense point cloud sequence. In experiment, we validate the performance of the proposed method on both synthetic and real world data. English IEEE Simultaneous Segmentation, Estimation and Analysis of Articulated Motion from Dense Point Cloud Sequence Conference 1 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.1085 - 1092 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 1085 1092 US Daejeon, SOUTH KOREA 2016-10-09 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016) 000391921701038 2-s2.0-85006516138