mlsanityData to 7 Oct 2026

NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Images

arXiv 2303.12012 ↗First posted 21 Mar 2023cs.CV cs.GRcited by 32

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Abstract (arXiv)

Recent progress in neural implicit functions has set new state-of-the-art in reconstructing high-fidelity 3D shapes from a collection of images. However, these approaches are limited to closed surfaces as they require the surface to be represented by a signed distance field. In this paper, we propose NeAT, a new neural rendering framework that can learn implicit surfaces with arbitrary topologies from multi-view images. In particular, NeAT represents the 3D surface as a level set of a signed distance function (SDF) with a validity branch for estimating the surface existence probability at the query positions. We also develop a novel neural volume rendering method, which uses SDF and validity to calculate the volume opacity and avoids rendering points with low validity. NeAT supports easy field-to-mesh conversion using the classic Marching Cubes algorithm. Extensive experiments on DTU, MGN, and Deep Fashion 3D datasets indicate that our approach is able to faithfully reconstruct both watertight and non-watertight surfaces. In particular, NeAT significantly outperforms the state-of-the-art methods in the task of open surface reconstruction both quantitatively and qualitatively.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 40 of its 56 references that are in the map. First posted on arXiv 21 Mar 2023.

Builds on

40 of 56 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  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. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  6. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  7. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  8. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  9. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  10. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 781
  11. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  12. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
28 more
  1. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
  2. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 587
  3. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
  4. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  5. 2017Neural 3D Mesh Renderercited by 437
  6. 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
  7. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 389
  8. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 332
  9. 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
  10. 2019Multi-Garment Net: Learning to Dress 3D People from Imagescited by 244
  11. 2019Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderercited by 221
  12. 2019DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracingcited by 179
  13. 2019SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimizationcited by 159
  14. 2017Learning Efficient Point Cloud Generation for Dense 3D Object Reconstructioncited by 133
  15. 2022HF-NeuS: Improved Surface Reconstruction Using High-Frequency Detailscited by 120
  16. 2019Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformationcited by 103
  17. 2019Learning to Infer Implicit Surfaces without 3D Supervisioncited by 103
  18. 2020Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Imagescited by 101
  19. 2018Unsupervised Training for 3D Morphable Model Regressioncited by 99
  20. 2021MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networkscited by 75
  21. 2022Neural Dual Contouringcited by 71
  22. 2022NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologiescited by 62
  23. 2022Vox-Surf: Voxel-based Implicit Surface Representationcited by 49
  24. 2022GIFS: Neural Implicit Function for General Shape Representationcited by 45
  25. 20183D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Imagecited by 40
  26. 2021Deep Implicit Surface Point Prediction Networkscited by 40
  27. 20223PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologiescited by 27
  28. 2020DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfacescited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 12; 2025: 6; 2026: 4.

32 papers.

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  1. 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,526
  2. 2025SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modelingcited by 76
  3. 2024Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Setcited by 47
  4. 2024DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shadingcited by 40
  5. 2023Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projectioncited by 36
  6. 2023Ghost on the Shell: An Expressive Representation of General 3D Shapescited by 24
  7. 2023Robust Zero Level-Set Extraction from Unsigned Distance Fields Based on Double Coveringcited by 21
  8. 2024VRP-UDF: Towards Unbiased Learning of Unsigned Distance Functions from Multi-view Images with Volume Rendering Priorscited by 17
  9. 2025GaussianUDF: Inferring Unsigned Distance Functions through 3D Gaussian Splattingcited by 14
  10. 2025Reconstructing 4D Spatial Intelligence: A Surveycited by 13
  11. 2023GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Cloudscited by 11
  12. 2023MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Drivingcited by 11
20 more
  1. 2025Scene4U: Hierarchical Layered 3D Scene Reconstruction from Single Panoramic Image for Your Immerse Explorationcited by 11
  2. 2023Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Modelscited by 8
  3. 2024Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capturecited by 8
  4. 20232S-UDF: A Novel Two-stage UDF Learning Method for Robust Non-watertight Model Reconstruction from Multi-view Imagescited by 7
  5. 2024A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functionscited by 7
  6. 2023Coordinate Quantized Neural Implicit Representations for Multi-view Reconstructioncited by 5
  7. 2024DCUDF2: Improving Efficiency and Accuracy in Extracting Zero Level Sets from Unsigned Distance Fieldscited by 3
  8. 2024Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstructioncited by 2
  9. 2024Convolutional Neural Networks and Vision Transformers for Fashion MNIST Classification: A Literature Reviewcited by 2
  10. 2024InfoNorm: Mutual Information Shaping of Normals for Sparse-View Reconstructioncited by 2
  11. 2024From Transparent to Opaque: Rethinking Neural Implicit Surfaces with α-NeuScited by 2
  12. 2026Affostruction: 3D Affordance Grounding with Generative Reconstructioncited by 1
  13. 20263D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstructioncited by 1
  14. 2023UNeR3D: Versatile and Scalable 3D RGB Point Cloud Generation from 2D Images in Unsupervised Reconstructioncited by 0
  15. 2024A Comparative Study of Neural Surface Reconstruction for Scientific Visualizationcited by 0
  16. 2024Gradient Distance Functioncited by 0
  17. 2025MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstructioncited by 0
  18. 2025GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filteringcited by 0
  19. 2026PCM-NeRF: Probabilistic Camera Modeling for Neural Radiance Fields under Pose Uncertaintycited by 0
  20. 2026DiffGI: Differentiable Geometry Images for High-Fidelity Thin-Shell 3D Generationcited by 0