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Sensors 2017, 17(4), 876;

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

School of Electrical & Electronic Engineering, Yonsei University, Seoul 03722, Korea
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)
Full-Text   |   PDF [3460 KB, uploaded 17 April 2017]   |  


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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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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