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PC²-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling

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Bisheng YangAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 2191 - 2201

https://doi.org/10.1145/3503161.3547777

Published: 10 October 2022 Publication History

Abstract

Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface. Existing methods divide the input points into small patches and upsample each patch separately, however, ignoring the global spatial consistency between patches. In this paper, we present a novel method PC$^2$-PU, which explores patch-to-patch and point-to-point correlations for more effective and robust point cloud upsampling. Specifically, our network has two appealing designs: (i) We take adjacent patches as supplementary inputs to compensate the loss structure information within a single patch and introduce a Patch Correlation Module to capture the difference and similarity between patches. (ii) After augmenting each patch's geometry, we further introduce a Point Correlation Module to reveal the relationship of points inside each patch to maintain the local spatial consistency. Extensive experiments on both synthetic and real scanned datasets demonstrate that our method surpasses previous upsampling methods, particularly with the noisy inputs. The code and data are at: https://github.com/chenlongwhu/PC2-PU.git.

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Cited By

Han YYin MYang FZhan F(2024)RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on ReconstructionApplied Sciences10.3390/app1415681414:15(6814)Online publication date: 5-Aug-2024
https://doi.org/10.3390/app14156814
Xu WDai WZheng ZLi CZou JXiong HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Point Cloud Upsampling with Geometric Algebra Driven Inverse Heat DissipationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681500(7385-7394)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681500
Zhang WWang ZXu LYang XLiu JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Informative Point cloud Dataset Extraction for Classification via Gradient-based Points MovingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680767(6384-6393)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680767
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Index Terms

PC²-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

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This work is licensed under a Creative Commons Attribution International 4.0 License.

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New York, NY, United States

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Published: 10 October 2022

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MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

Lisboa, Portugal

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Cited By

Han YYin MYang FZhan F(2024)RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on ReconstructionApplied Sciences10.3390/app1415681414:15(6814)Online publication date: 5-Aug-2024
https://doi.org/10.3390/app14156814
Xu WDai WZheng ZLi CZou JXiong HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Point Cloud Upsampling with Geometric Algebra Driven Inverse Heat DissipationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681500(7385-7394)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681500
Zhang WWang ZXu LYang XLiu JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Informative Point cloud Dataset Extraction for Classification via Gradient-based Points MovingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680767(6384-6393)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680767
Zhang WRahmani HYang XLiu JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Reverse2Complete: Unpaired Multimodal Point Cloud Completion via Guided DiffusionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680590(5892-5901)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680590
Chen CJin AWang ZZheng YYang BZhou JXu YTu Z(2024)SGSR-Net: Structure Semantics Guided LiDAR Super-Resolution Network for Indoor LiDAR SLAMIEEE Transactions on Multimedia10.1109/TMM.2023.328975226(1842-1854)Online publication date: 2024
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Bu TZhu JMa TJiang S(2024)Pavement Point Cloud Upsampling Based on Transformer: Toward Enhancing 3D Pavement DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.345430925:12(21647-21657)Online publication date: Dec-2024
https://doi.org/10.1109/TITS.2024.3454309
Kim JBang GChoi KSeong MYoo JPyo EChoi J(2024)PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10611144(9117-9124)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10611144
Liu YChen RLi YLi YTan X(2024)SPU-PMD: Self-Supervised Point Cloud Upsampling via Progressive Mesh Deformation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00496(5188-5197)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00496
Li YLiu YChen RLi HZhao N(2024)Point cloud upsampling via a coarse-to-fine network with transformer-encoderThe Visual Computer10.1007/s00371-024-03535-8Online publication date: 21-Jun-2024
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Wang WKong XWang DZhao X(2024)Sparse Point Cloud Upsampling Based on Neural Implicit FunctionsAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5600-1_32(369-380)Online publication date: 30-Jul-2024
https://doi.org/10.1007/978-981-97-5600-1_32
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