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A Comparative Study of Neural Surface Reconstruction for Scientific Visualization

arXiv 2407.20868 ↗First posted 30 Jul 2024cs.GR cs.CVcited by 0

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Abstract (arXiv)

This comparative study evaluates various neural surface reconstruction methods, particularly focusing on their implications for scientific visualization through reconstructing 3D surfaces via multi-view rendering images. We categorize ten methods into neural radiance fields and neural implicit surfaces, uncovering the benefits of leveraging distance functions (i.e., SDFs and UDFs) to enhance the accuracy and smoothness of the reconstructed surfaces. Our findings highlight the efficiency and quality of NeuS2 for reconstructing closed surfaces and identify NeUDF as a promising candidate for reconstructing open surfaces despite some limitations. By sharing our benchmark dataset, we invite researchers to test the performance of their methods, contributing to the advancement of surface reconstruction solutions for scientific visualization.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 30 Jul 2024.

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28 of 41 references.

References · 28

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  5. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  6. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  7. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  8. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  9. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  10. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  11. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  12. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
16 more
  1. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 587
  2. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  3. 2023Neuralangelo: High-Fidelity Neural Surface Reconstructioncited by 464
  4. 2022NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstructioncited by 311
  5. 2019Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Imagescited by 164
  6. 2017Learning Efficient Point Cloud Generation for Dense 3D Object Reconstructioncited by 133
  7. 2020MeshSDF: Differentiable Iso-Surface Extractioncited by 112
  8. 2019Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformationcited by 103
  9. 2021MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networkscited by 75
  10. 2022NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologiescited by 62
  11. 2022Vox-Surf: Voxel-based Implicit Surface Representationcited by 49
  12. 20183D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Imagecited by 40
  13. 2023NeUDF: Leaning Neural Unsigned Distance Fields with Volume Renderingcited by 40
  14. 2022DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualizationcited by 32
  15. 2023NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Imagescited by 32
  16. 2020Learning Adaptive Sampling and Reconstruction for Volume Visualizationcited by 14

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