Space-Time Co-Segmentation of Articulated Point Cloud Sequences.
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| Title: | Space-Time Co-Segmentation of Articulated Point Cloud Sequences. |
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| Authors: | Yuan, Qing1, Li, Guiqing1, Xu, Kai2,3, Chen, Xudong1, Huang, Hui2,4 |
| Source: | Computer Graphics Forum. May2016, Vol. 35 Issue 2, p419-429. 11p. 15 Color Photographs, 2 Charts. |
| Subjects: | Computer graphics research, Computer art, Digital image processing, Computational geometry, Computer science |
| Abstract: | Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter-frame variation across the whole sequence. We propose a local-to-global approach to co-segment point cloud sequences of articulated objects into near-rigid moving parts. Our method starts from a per-frame point clustering, derived from a robust voting-based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novel space-time segment grouping technqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co-segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter-frame variation across the whole sequence. We propose a local-to-global approach to co-segment point cloud sequences of articulated objects into near-rigid moving parts. Our method starts from a per-frame point clustering, derived from a robust voting-based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novel space-time segment grouping technqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co-segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01677055 |
| DOI: | 10.1111/cgf.12843 |
