mlsanityData to 7 Oct 2026

Optimizing Implicit Neural Representations from Point Clouds via Energy-Based Models

arXiv 2311.02601 ↗First posted 5 Nov 2023cs.CV cs.LGcited by 1

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Abstract (arXiv)

Reconstructing a continuous surface from an unoritented 3D point cloud is a fundamental task in 3D shape processing. In recent years, several methods have been proposed to address this problem using implicit neural representations (INRs). In this study, we propose a method to optimize INRs using energy-based models (EBMs). By employing the absolute value of the coordinate-based neural networks as the energy function, the INR can be optimized through the estimation of the point cloud distribution by the EBM. In addition, appropriate parameter settings of the EBM enable the model to consider the magnitude of point cloud noise. Our experiments confirmed that the proposed method is more robust against point cloud noise than conventional surface reconstruction methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 44 references that are in the map. First posted on arXiv 5 Nov 2023.

Builds on

30 of 44 references.

References · 30

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  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
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18 more
  1. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
  2. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  3. 2021How to Train Your Energy-Based Modelscited by 271
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  6. 2020Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfacescited by 146
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  13. 2020Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classificationcited by 57
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  16. 2020Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Cloudscited by 30
  17. 2020Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysiscited by 26
  18. 2021LightSAL: Lightweight Sign Agnostic Learning for Implicit Surface Representationcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Energy-Based Open-Set Active Learning for Object Classificationcited by 0