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Sensors 2017, 17(4), 876; doi:10.3390/s17040876

New Compact 3-Dimensional Shape Descriptor for a Depth Camera in Indoor Environments

1
School of Electrical & Electronic Engineering, Yonsei University, Seoul 03722, Korea
2
LG Electronics, Seoul 08592, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 31 December 2016 / Revised: 7 April 2017 / Accepted: 11 April 2017 / Published: 16 April 2017
(This article belongs to the Section Physical Sensors)
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Abstract

This study questions why existing local shape descriptors have high dimensionalities (up to hundreds) despite simplicity of local shapes. We derived an answer from a historical context and provided an alternative solution by proposing a new compact descriptor. Although existing descriptors can express complicated shapes and depth sensors have been improved, complex shapes are rarely observed in an ordinary environment and a depth sensor only captures a single side of a surface with noise. Therefore, we designed a new descriptor based on principal curvatures, which is compact but practically useful. For verification, the CoRBS dataset, the RGB-D Scenes dataset and the RGB-D Object dataset were used to compare the proposed descriptor with existing descriptors in terms of shape, instance, and category recognition rate. The proposed descriptor showed a comparable performance with existing descriptors despite its low dimensionality of 4. View Full-Text
Keywords: shape descriptor; correspondence; point cloud; principal curvature shape descriptor; correspondence; point cloud; principal curvature
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Choi, H.; Kim, E. New Compact 3-Dimensional Shape Descriptor for a Depth Camera in Indoor Environments. Sensors 2017, 17, 876.

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