Semantic Feature Mining for 3D Object Classification and Segmentation

Lu, W. , Zhao, D. , Premebida, C., Chen, W.-H. and Tian, D. (2021) Semantic Feature Mining for 3D Object Classification and Segmentation. In: 2021 International Conference on Robotics and Automation (ICRA 2021), Xi’an, China, 30 May-5 June 2021, pp. 13539-13545. ISBN 9781728190778 (doi: 10.1109/ICRA48506.2021.9561986)

Abstract

Deep learning on 3D point clouds has drawn much attention, due to its large variety of applications in intelligent perception for automated and robotic systems. Unlike structured 2D images, it is challenging to extract features and implement convolutional networks over these unordered points. Although a number of previous works achieved high accuracies for point cloud recognition, they tend to process local point information in such a way that semantic information is not fully encoded. In this paper, we propose a deep neural network for 3D point cloud processing that utilizes effective feature aggregation methods emphasizing both generalizability and relevance. In particular, our method uses fixed-radius grouping for pooling layers and spherical kernel convolution for semantics mining. To address the issue of gradient degradation and memory consumption of a deep network, a parallel feature feed-forward mechanism and bottleneck layers are implemented to reduce the number of parameters. Experiments show that our algorithm achieves state-of-the-art results and competitive accuracy in both classification and part segmentation while maintaining an efficient architecture.

• Item Type: Conference Proceedings
• Additional Information: This work was sponsored in part by the Engineering and Physical Sciences Research Council of the UK under the EPSRC Innovation Fellowship (EP/S001956/1), in part by the Newton Advanced Fellowship (UKChina International Cooperation and Exchange Project) jointly supported by the UK Royal Society (NAFnR1n201213) ) and the National Natural Science Foundation of China (62061130221), and in part by the State Key Laboratory of Automotive Safety and Energy under Project No. KF2009.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong and Lu, Weihao
• Authors: Lu, W., Zhao, D., Premebida, C., Chen, W.-H., and Tian, D.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISSN: 2577-087X
• ISBN: 9781728190778
• Copyright Holders: Copyright © 2021 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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