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Space-Time Co-Segmentation of Articulated Point Cloud Sequences.

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Title: Space-Time Co-Segmentation of Articulated Point Cloud Sequences.
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]
Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: 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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  Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/cgf.12843
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        Text: English
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      – SubjectFull: Digital image processing
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              M: 05
              Text: May2016
              Type: published
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