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3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection
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Du, Liang, Tan, Jingang, Xue, Xiangyang, Chen, Lili, Wen, Hongkai, Feng, Jianfeng, Li, Jiamao and Zhang, Xiaolin (2020) 3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection. In: IEEE International Conference on Robotics and Automaion, Paris, France, 31 May - 4 Jun 2020. Published in: 2020 IEEE International Conference on Robotics and Automation (ICRA) ISBN 9781728173962. ISSN 1050-4729. doi:10.1109/ICRA40945.2020.9197242
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WRAP-3DCFS-Fast-joint-3D-semantic-segmentation-Wen-2020.pdf - Accepted Version - Requires a PDF viewer. Download (5Mb) | Preview |
Official URL: https://doi.org/10.1109/ICRA40945.2020.9197242
Abstract
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost.
| Item Type: | Conference Item (Paper) | ||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TS Manufactures |
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| Divisions: | Faculty of Science > Computer Science | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Three-dimensional printing , Image segmentation , Image processing -- Digital techniques , Computer vision | ||||||||||||||||||
| Journal or Publication Title: | 2020 IEEE International Conference on Robotics and Automation (ICRA) | ||||||||||||||||||
| Publisher: | IEEE | ||||||||||||||||||
| ISBN: | 9781728173962 | ||||||||||||||||||
| ISSN: | 1050-4729 | ||||||||||||||||||
| Official Date: | 15 September 2020 | ||||||||||||||||||
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| DOI: | 10.1109/ICRA40945.2020.9197242 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Publisher Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | ||||||||||||||||||
| Title of Event: | IEEE International Conference on Robotics and Automaion | ||||||||||||||||||
| Type of Event: | Conference | ||||||||||||||||||
| Location of Event: | Paris, France | ||||||||||||||||||
| Date(s) of Event: | 31 May - 4 Jun 2020 |
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