research-article

Learning modified indicator functions for surface reconstruction▪

Authors:

Bin WangAuthors Info & Claims

Volume 102, Issue C

Pages 309 - 319

https://doi.org/10.1016/j.cag.2021.10.017

Published: 01 February 2022 Publication History

Abstract

Surface reconstruction is a fundamental problem in 3D graphics. In this paper, we propose a learning-based approach for implicit surface reconstruction from raw point clouds without normals. Our method is inspired by Gauss Lemma in potential energy theory, which gives an explicit integral formula for the indicator functions. We design a novel deep neural network to perform surface integral and learn the modified indicator functions from un-oriented and noisy point clouds. We concatenate features with different scales for accurate point-wise contributions to the integral. Moreover, we propose a novel Surface Element Feature Extractor to learn local shape properties. Experiments show that our method generates smooth surfaces with high normal consistency from point clouds with different noise scales and achieves state-of-the-art reconstruction performance compared with current data-driven and non-data-driven approaches.

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Highlights

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A learning-based approach for surface reconstruction from raw point clouds.

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A novel Surface Element Feature Extractor to learn local shape properties.

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Aggregating the point-wise contributions of Gauss Lemma in the network.

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Generating smooth surfaces with high normal consistency.

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Cited By

Huang SChen GChen LHuang HSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)NeuralIndicatorProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692861(19622-19634)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692861
Huang ZWen YWang ZRen JJia K(2024)Surface Reconstruction From Point Clouds: A Survey and a BenchmarkIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.342920946:12(9727-9748)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3429209
Xiao DShi ZLi SDeng BWang B(2023)Point normal orientation and surface reconstruction by incorporating isovalue constraints to Poisson equationComputer Aided Geometric Design10.1016/j.cagd.2023.102195103:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.cagd.2023.102195
Show More Cited By

Index Terms

Learning modified indicator functions for surface reconstruction▪
1. Computing methodologies
2. Theory of computation
  1. Randomness, geometry and discrete structures

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Computers and Graphics

Computers and Graphics Volume 102, Issue C

Feb 2022

670 pages

ISSN:0097-8493

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2022

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Cited By

Huang SChen GChen LHuang HSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)NeuralIndicatorProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692861(19622-19634)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692861
Huang ZWen YWang ZRen JJia K(2024)Surface Reconstruction From Point Clouds: A Survey and a BenchmarkIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.342920946:12(9727-9748)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3429209
Xiao DShi ZLi SDeng BWang B(2023)Point normal orientation and surface reconstruction by incorporating isovalue constraints to Poisson equationComputer Aided Geometric Design10.1016/j.cagd.2023.102195103:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.cagd.2023.102195
Biasotti SLiu YZhu B(2022)Foreword to the Special Issue on Shape Modeling International 2021 (SMI2021)Computers and Graphics10.1016/j.cag.2022.03.001103:C(A7-A9)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.cag.2022.03.001

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