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Robust point cloud normal estimation via multi-level critical point aggregation

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Abstract

We propose a multi-level critical point aggregation architecture based on a graph attention mechanism for 3D point cloud normal estimation, which can efficiently focus on locally important points during the feature extraction process. Wherein, the local feature aggregation (LFA) module and the global feature refinement (GFR) module are designed to accurately identify critical points which are geometrically closer to tangent plane for surface fitting at both local and global levels. Specifically, the LFA module captures significant local information from neighboring points with strong geometric correlations to the query point in the low-level feature space. The GFR module enhances the exploration of global geometric correlations in the high-level feature space, allowing the network to focus precisely on critical global points. To address indistinguishable features in the low-level space, we implement a stacked LFA structure. This structure transfers essential adjacent information across multiple levels, enabling deep feature aggregation layer by layer. Then the GFR module can leverage robust local geometric information and refines it into comprehensive global features. Our multi-level point-aware architecture improves the stability and accuracy of surface fitting and normal estimation, even in the presence of sharp features, high noise or anisotropic structures. Experimental results demonstrate that our method is competitive and achieves stable performance on both synthetic and real-world datasets. Code is available at https://github.com/CharlesLee96/NormalEstimation.

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Data availability

The training and test data have been deposited in the web of Github: https://github.com/LeoQLi/HSurf-Net, and our code can be obtain at : https://github.com/CharlesLee96/NormalEstimation

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Acknowledgements

This research was supported in part by the Natural Science Foundation of China under Grants 62002040, in part by China Postdoctoral Science Foundation 2021M690501, in part by the Science Foundation of Zhejiang Sci-Tech University under Grant Number 22062338-Y and in part by Bei**g Postdoctoral Science Foundation under Grant Number 2022-ZZ-069.

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Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Jun Zhou, Yaoshun Li, Nannan Li & Zhiyang Li

  2. Department of mathematics, Zhejiang Sci-Tech University, Hangzhou, China

    Mingjie Wang

  3. School of Engineering, University of British Columbia, Vancouver, Canada

    Weixiao Wang

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  1. Jun Zhou
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  2. Yaoshun Li
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Contributions

Zhou and Yaoshun Li wrote the main manuscript text and revised it. Nannan Li and Zhiyang Li prepared Figs. 1, 2, 3, 4, 5 and 6; Mingjie Wang and Weixiao Wang reviewed the manuscript.

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Correspondence to Jun Zhou.

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Zhou, J., Li, Y., Wang, M. et al. Robust point cloud normal estimation via multi-level critical point aggregation. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03532-x

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