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Neural Gradient Learning and Optimization for Oriented Point Normal Estimation

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Zhizhong HanAuthors Info & Claims

SA '23: SIGGRAPH Asia 2023 Conference Papers

Article No.: 122, Pages 1 - 9

https://doi.org/10.1145/3610548.3618253

Published: 11 December 2023 Publication History

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Abstract

We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excellent gradient approximation properties for the underlying geometry of the data. We utilize a simple neural network to parameterize the objective function to produce gradients at points using a global implicit representation. However, the derived gradients usually drift away from the ground-truth oriented normals due to the lack of local detail descriptions. Therefore, we introduce Gradient Vector Optimization (GVO) to learn an angular distance field based on local plane geometry to refine the coarse gradient vectors. Finally, we formulate our method with a two-phase pipeline of coarse estimation followed by refinement. Moreover, we integrate two weighting functions, i.e., anisotropic kernel and inlier score, into the optimization to improve the robust and detail-preserving performance. Our method efficiently conducts global gradient approximation while achieving better accuracy and generalization ability of local feature description. This leads to a state-of-the-art normal estimator that is robust to noise, outliers and point density variations. Extensive evaluations show that our method outperforms previous works in both unoriented and oriented normal estimation on widely used benchmarks. The source code and pre-trained models are available at https://github.com/LeoQLi/NGLO .

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Cited By

Chen CLiu YHan Z(2025)NeuralTPS: Learning Signed Distance Functions Without Priors From Single Sparse Point CloudsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.347634947:1(565-582)Online publication date: Jan-2025
https://doi.org/10.1109/TPAMI.2024.3476349
Jin WZhou JWang MLi NWang WLiu X(2025)Enhanced normal estimation of point clouds via fine-grained geometric information learningMachine Vision and Applications10.1007/s00138-025-01671-236:2Online publication date: 22-Feb-2025
https://doi.org/10.1007/s00138-025-01671-2
Li QFeng HShi KGao YFang YLiu YHan Z(2024)Learning Signed Hyper Surfaces for Oriented Point Cloud Normal EstimationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.343122146:12(9957-9974)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3431221

Index Terms

Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
  2. Computer graphics
    1. Shape modeling
      1. Mesh models
      2. Point-based models

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cover image ACM Conferences

SA '23: SIGGRAPH Asia 2023 Conference Papers

December 2023

1113 pages

ISBN:9798400703157

DOI:10.1145/3610548

Copyright © 2023 ACM.

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Published: 11 December 2023

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National Key R&D Program of China
National Natural Science Foundation of China

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SA '23

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SIGGRAPH

SA '23: SIGGRAPH Asia 2023

December 12 - 15, 2023

NSW, Sydney, Australia

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Cited By

Chen CLiu YHan Z(2025)NeuralTPS: Learning Signed Distance Functions Without Priors From Single Sparse Point CloudsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.347634947:1(565-582)Online publication date: Jan-2025
https://doi.org/10.1109/TPAMI.2024.3476349
Jin WZhou JWang MLi NWang WLiu X(2025)Enhanced normal estimation of point clouds via fine-grained geometric information learningMachine Vision and Applications10.1007/s00138-025-01671-236:2Online publication date: 22-Feb-2025
https://doi.org/10.1007/s00138-025-01671-2
Li QFeng HShi KGao YFang YLiu YHan Z(2024)Learning Signed Hyper Surfaces for Oriented Point Cloud Normal EstimationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.343122146:12(9957-9974)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3431221

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