mlsanityData to 14 Oct 2026

Sensing Surface Patches in Volume Rendering for Inferring Signed Distance Functions

arXiv 2412.16467 ↗First posted 21 Dec 2024cs.CVcited by 2

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Abstract (arXiv)

It is vital to recover 3D geometry from multi-view RGB images in many 3D computer vision tasks. The latest methods infer the geometry represented as a signed distance field by minimizing the rendering error on the field through volume rendering. However, it is still challenging to explicitly impose constraints on surfaces for inferring more geometry details due to the limited ability of sensing surfaces in volume rendering. To resolve this problem, we introduce a method to infer signed distance functions (SDFs) with a better sense of surfaces through volume rendering. Using the gradients and signed distances, we establish a small surface patch centered at the estimated intersection along a ray by pulling points randomly sampled nearby. Hence, we are able to explicitly impose surface constraints on the sensed surface patch, such as multi-view photo consistency and supervision from depth or normal priors, through volume rendering. We evaluate our method by numerical and visual comparisons on scene benchmarks. Our superiority over the latest methods justifies our effectiveness.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 40 of its 54 references that are in the map. First posted on arXiv 21 Dec 2024.

Builds on

40 of 54 references.

References · 40

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  2. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  3. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  4. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  5. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,076
  6. 2021Volume Rendering of Neural Implicit Surfacescited by 996
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  10. 2021DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Camerascited by 687
  11. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  12. 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 616
28 more
  1. 2021NICE-SLAM: Neural Implicit Scalable Encoding for SLAMcited by 589
  2. 2023Neuralangelo: High-Fidelity Neural Surface Reconstructioncited by 467
  3. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
  4. 2021Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scanscited by 336
  5. 2022Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstructioncited by 212
  6. 2021SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformercited by 164
  7. 2023NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAMcited by 164
  8. 2019SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimizationcited by 159
  9. 2020Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfacescited by 147
  10. 2022NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priorscited by 125
  11. 2022Neural 3D Scene Reconstruction with the Manhattan-world Assumptioncited by 123
  12. 2022HF-NeuS: Improved Surface Reconstruction Using High-Frequency Detailscited by 121
  13. 2021DiGS : Divergence guided shape implicit neural representation for unoriented point cloudscited by 77
  14. 2021Deep Implicit Moving Least-Squares Functions for 3D Reconstructioncited by 67
  15. 2022Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priorscited by 60
  16. 2021SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networkscited by 59
  17. 2024Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Setcited by 49
  18. 2023Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignmentcited by 39
  19. 2023Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projectioncited by 36
  20. 2020DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Imagescited by 35
  21. 2022GenSDF: Two-Stage Learning of Generalizable Signed Distance Functionscited by 30
  22. 2023Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priorscited by 28
  23. 2023Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mappingcited by 27
  24. 2022Latent Partition Implicit with Surface Codes for 3D Representationcited by 21
  25. 2024VRP-UDF: Towards Unbiased Learning of Unsigned Distance Functions from Multi-view Images with Volume Rendering Priorscited by 17
  26. 2023Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priorscited by 11
  27. 2024MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Stepcited by 11
  28. 2024Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priorscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstructioncited by 12
  2. 2026SGAD-SLAM: Splatting Gaussians at Adjusted Depth for Better Radiance Fields in RGBD SLAMcited by 1