On creating low dimensional 3D feature descriptors with PCA
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 applicati...
| Hlavní autoři: | , , , , |
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| Další autoři: | |
| Médium: | Conference Paper |
| Jazyk: | English |
| Vydáno: |
2020
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| Témata: | |
| On-line přístup: | https://hdl.handle.net/10356/140477 |
| _version_ | 1826129991581040640 |
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| author | Prakhya, Sai Manoj Liu, Bingbing Lin, Weisi Li, Kun Xiao, Yong |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Prakhya, Sai Manoj Liu, Bingbing Lin, Weisi Li, Kun Xiao, Yong |
| author_sort | Prakhya, Sai Manoj |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:49:16Z |
| format | Conference Paper |
| id | ntu-10356/140477 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:49:16Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1404772020-05-29T07:36:44Z On creating low dimensional 3D feature descriptors with PCA Prakhya, Sai Manoj Liu, Bingbing Lin, Weisi Li, Kun Xiao, Yong School of Computer Science and Engineering 2017 IEEE Region 10 Conference (TENCON 2017) Engineering::Computer science and engineering Three-dimensional Displays Principal Component Analysis 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. ASTAR (Agency for Sci., Tech. and Research, S’pore) 2020-05-29T07:36:43Z 2020-05-29T07:36:43Z 2017 Conference Paper 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 978-1-5090-1135-3 https://hdl.handle.net/10356/140477 10.1109/TENCON.2017.8227882 2-s2.0-85044219643 315 320 en © 2017 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Three-dimensional Displays Principal Component Analysis Prakhya, Sai Manoj Liu, Bingbing Lin, Weisi Li, Kun Xiao, Yong On creating low dimensional 3D feature descriptors with PCA |
| title | On creating low dimensional 3D feature descriptors with PCA |
| title_full | On creating low dimensional 3D feature descriptors with PCA |
| title_fullStr | On creating low dimensional 3D feature descriptors with PCA |
| title_full_unstemmed | On creating low dimensional 3D feature descriptors with PCA |
| title_short | On creating low dimensional 3D feature descriptors with PCA |
| title_sort | on creating low dimensional 3d feature descriptors with pca |
| topic | Engineering::Computer science and engineering Three-dimensional Displays Principal Component Analysis |
| url | https://hdl.handle.net/10356/140477 |
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