mlsanityData to 7 Oct 2026

Neuralangelo: High-Fidelity Neural Surface Reconstruction

arXiv 2306.03092 ↗First posted 5 Jun 2023cs.CVcited by 464

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Abstract (arXiv)

Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes. To address the issue, we present Neuralangelo, which combines the representation power of multi-resolution 3D hash grids with neural surface rendering. Two key ingredients enable our approach: (1) numerical gradients for computing higher-order derivatives as a smoothing operation and (2) coarse-to-fine optimization on the hash grids controlling different levels of details. Even without auxiliary inputs such as depth, Neuralangelo can effectively recover dense 3D surface structures from multi-view images with fidelity significantly surpassing previous methods, enabling detailed large-scale scene reconstruction from RGB video captures.

Key facts. Cited by 464 papers in the map (data through 7 Oct 2026). Builds on 34 of its 56 references that are in the map. First posted on arXiv 5 Jun 2023.

Builds on

34 of 56 references.

References · 34

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  2. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  3. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  4. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  5. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  6. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  7. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,194
  8. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  9. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  10. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 857
  11. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 780
  12. 2020Implicit Geometric Regularization for Learning Shapescited by 707
22 more
  1. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  2. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
  3. 2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 641
  4. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 569
  5. 2022MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstructioncited by 467
  6. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
  7. 2022NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstructioncited by 311
  8. 2018DeepMVS: Learning Multi-view Stereopsiscited by 277
  9. 2022Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstructioncited by 211
  10. 2019SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimizationcited by 159
  11. 2019Point-Based Multi-View Stereo Networkcited by 147
  12. 2021Improving neural implicit surfaces geometry with patch warpingcited by 136
  13. 2021NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illuminationcited by 135
  14. 2022HF-NeuS: Improved Surface Reconstruction Using High-Frequency Detailscited by 120
  15. 2021NeRF for Outdoor Scene Relightingcited by 114
  16. 2022Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstructioncited by 92
  17. 2022Neural 3D Reconstruction in the Wildcited by 79
  18. 2021Learning Signed Distance Field for Multi-view Surface Reconstructioncited by 76
  19. 2020SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Imagescited by 68
  20. 2022Human Performance Modeling and Rendering via Neural Animated Meshcited by 64
  21. 2022Critical Regularizations for Neural Surface Reconstruction in the Wildcited by 51
  22. 2022Vox-Surf: Voxel-based Implicit Surface Representationcited by 49

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 61; 2024: 195; 2025: 131; 2026: 77.

464 papers.

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  1. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,061
  2. 2023DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creationcited by 837
  3. 2023Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstructioncited by 753
  4. 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 611
  5. 2023GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splattingcited by 331
  6. 2025GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Controlcited by 325
  7. 2024PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstructioncited by 280
  8. 2024Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenescited by 226
  9. 2024Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussianscited by 225
  10. 2024VastGaussian: Vast 3D Gaussians for Large Scene Reconstructioncited by 209
  11. 2024High-quality Surface Reconstruction using Gaussian Surfelscited by 159
  12. 2023NeuRAD: Neural Rendering for Autonomous Drivingcited by 146
38 more
  1. 2023Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Camerascited by 139
  2. 2023NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidancecited by 137
  3. 2024DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshingcited by 136
  4. 2024RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPScited by 110
  5. 2024RaDe-GS: Rasterizing Depth in Gaussian Splattingcited by 110
  6. 2024SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videoscited by 100
  7. 2024MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstructioncited by 94
  8. 2023VR-NeRF: High-Fidelity Virtualized Walkable Spacescited by 86
  9. 2024FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenescited by 86
  10. 2023Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Renderingcited by 84
  11. 2024GSDF: 3DGS Meets SDF for Improved Rendering and Reconstructioncited by 81
  12. 20243DGSR: Implicit Surface Reconstruction with 3D Gaussian Splattingcited by 78
  13. 2023FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functionscited by 77
  14. 2024Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splattingcited by 73
  15. 2024How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Surveycited by 72
  16. 2024GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo Viewscited by 65
  17. 2024FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descentcited by 61
  18. 2024VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstructioncited by 50
  19. 2024CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenescited by 48
  20. 2024Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Setcited by 47
  21. 2024EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesiscited by 46
  22. 2024PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistencycited by 42
  23. 2024Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesiscited by 41
  24. 2025GenFusion: Closing the Loop between Reconstruction and Generation via Videoscited by 41
  25. 2024Textured Gaussians for Enhanced 3D Scene Appearance Modelingcited by 40
  26. 2023Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projectioncited by 36
  27. 2024Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Renderingcited by 35
  28. 2025MILo: Mesh-In-the-Loop Gaussian Splatting for Detailed and Efficient Surface Reconstructioncited by 35
  29. 2024EnvGS: Modeling View-Dependent Appearance with Environment Gaussiancited by 34
  30. 2024GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstructioncited by 31
  31. 2024Robust Incremental Structure-from-Motion with Hybrid Featurescited by 31
  32. 2023SpecNeRF: Gaussian Directional Encoding for Specular Reflectionscited by 30
  33. 2025FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using Gaussian Splatting with Depth-Feature Consistencycited by 30
  34. 2025Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigationcited by 30
  35. 2023UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflectionscited by 29
  36. 2024Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splattingcited by 29
  37. 2025DRAWER: Digital Reconstruction and Articulation With Environment Realismcited by 29
  38. 2025What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Modelscited by 29

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