research-article

Exploring Dual Representations in Large-Scale Point Clouds: A Simple Weakly Supervised Semantic Segmentation Framework

Authors:

Cai XuAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 2371 - 2380

https://doi.org/10.1145/3581783.3612224

Published: 27 October 2023 Publication History

Abstract

Existing work shows that 3D point clouds produce only about a 4% drop in semantic segmentation even at 1% random point annotation, which inspires us to further explore how to achieve better results at lower cost. As scene point clouds provide position and color information and often used in tandem as the only input, with little work going into segmentation by fusing information from dual spaces. To optimize point cloud representations, we propose a novel framework for the dual representation query network (DRQNet). The proposed framework partitions the input point cloud into position and color spaces, using the separately extracted geometric structure and semantic context to create an internal supervisory mechanism that bridges the dual spaces and fuses the information. Adopting sparsely annotated points as the query set, DRQNet provide guidance and perceptual information for multi-stage point clouds through random sampling. More, to differentiate and enhance the features generated by local neighbourhoods within multiple perceptual fields, we design a representation selection module to identify the contributions made by the position and color of each query point, and weight them adaptively according to reliability. The proposed DRQNet is robust to point cloud analysis and eliminates the effects of irregularities and disorder. Our method achieves significant performance gains on three mainstream benchmarks.

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Cited By

Han XRen YCong PSun YWang JXu LMa YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Gait Recognition in Large-scale Free Environment via Single LiDARProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681166(380-389)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681166
Han XRen YYao YSun YMa YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Towards Practical Human Motion Prediction with LiDAR Point CloudsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680720(7629-7638)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680720
Liu YHu QGuo Y(2024)BSTS: A Weakly-Supervised Method for Semantic Learning of 3D Point CloudsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.342015034:11(11386-11399)Online publication date: Nov-2024
https://doi.org/10.1109/TCSVT.2024.3420150
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Index Terms

Exploring Dual Representations in Large-Scale Point Clouds: A Simple Weakly Supervised Semantic Segmentation Framework
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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Natural Science Basic Research Plan in Shaanxi Province of China
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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Han XRen YCong PSun YWang JXu LMa YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Gait Recognition in Large-scale Free Environment via Single LiDARProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681166(380-389)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681166
Han XRen YYao YSun YMa YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Towards Practical Human Motion Prediction with LiDAR Point CloudsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680720(7629-7638)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680720
Liu YHu QGuo Y(2024)BSTS: A Weakly-Supervised Method for Semantic Learning of 3D Point CloudsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.342015034:11(11386-11399)Online publication date: Nov-2024
https://doi.org/10.1109/TCSVT.2024.3420150
Wu YLiu JGong MMiao QMa WXu C(2024)Joint Semantic Segmentation using representations of LiDAR point clouds and camera imagesInformation Fusion10.1016/j.inffus.2024.102370108:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102370
Wang HZhang DLiu TQi X(2024)Neighborhood Feature Enhancement Flow Diffusion Model for Point Cloud GenerationPattern Recognition10.1007/978-3-031-78389-0_23(339-354)Online publication date: 5-Dec-2024
https://doi.org/10.1007/978-3-031-78389-0_23

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