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Title: On creating low dimensional 3D feature descriptors with PCA
Authors: Prakhya, Sai Manoj
Liu, Bingbing
Lin, Weisi
Li, Kun
Xiao, Yong
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Prakhya, S. M., Liu, B., Lin, W., Li, K., & Xiao, Y. (2017). On creating low dimensional 3D feature descriptors with PCA. Proceedings of 2017 IEEE Region 10 Conference (TENCON 2017), 315-320. doi:10.1109/TENCON.2017.8227882
Abstract: With the availability of commodity depth sensors and depth sensing capabilities on mobile devices, there is a need to develop memory efficient and computationally cheap applications. 3D feature descriptor based keypoint matching is the very first step in various computer vision and robotic applications, and is also one of the most memory and computationally demanding steps. In this work, we apply Principal Component Analysis (PCA) to reduce the dimensionality of 3D feature descriptors and show that they retain their descriptiveness, while drastically decreasing their memory and computational requirements. We apply this scalable PCA based dimensionality reduction technique on three state-of-the-art 3D feature descriptors and quantitatively evaluate their performance with varying dimensionality. Experimental results on publicly available Bologna Kinect dataset show that the proposed method reduces the dimensionality of SHOT, RoPS and FPFH feature descriptors from 352 to 50, 135 to 30 and 33 to 15, respectively, while offering more than 90% of the keypoint matching performance.
ISBN: 978-1-5090-1135-3
DOI: 10.1109/TENCON.2017.8227882
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers


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