mlsanityData to 7 Oct 2026

Deep Surface Reconstruction from Point Clouds with Visibility Information

arXiv 2202.01810 ↗First posted 3 Feb 2022cs.CVcited by 8

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Abstract (arXiv)

Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations. Sensor visibility, however, holds meaningful information regarding space occupancy and surface orientation. In this paper, we present two simple ways to augment raw point clouds with visibility information, so it can directly be leveraged by surface reconstruction networks with minimal adaptation. Our proposed modifications consistently improve the accuracy of generated surfaces as well as the generalization ability of the networks to unseen shape domains. Our code and data is available at https://github.com/raphaelsulzer/dsrv-data.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

32 of 53 references.

References · 32

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  3. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  5. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  6. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  7. 2020Convolutional Occupancy Networkscited by 760
  8. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  9. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
  10. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 587
  11. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 389
  12. 2020Local Implicit Grid Representations for 3D Scenescited by 372
20 more
  1. 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
  2. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  3. 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
  4. 2021Shape As Points: A Differentiable Poisson Solvercited by 186
  5. 2020Points2Surf: Learning Implicit Surfaces from Point Cloud Patchescited by 122
  6. 2020SALD: Sign Agnostic Learning with Derivativescited by 115
  7. 2022POCO: Point Convolution for Surface Reconstructioncited by 91
  8. 2019SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scanscited by 77
  9. 2019ConvPoint: Continuous Convolutions for Point Cloud Processingcited by 62
  10. 2021SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networkscited by 59
  11. 2020Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networkscited by 52
  12. 2020Point2Mesh: A Self-Prior for Deformable Meshescited by 41
  13. 2020ManifoldPlus: A Robust and Scalable Watertight Manifold Surface Generation Method for Triangle Soupscited by 38
  14. 2020PointTriNet: Learned Triangulation of 3D Point Setscited by 32
  15. 2021Orienting Point Clouds with Dipole Propagationcited by 31
  16. 2020Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Cloudscited by 30
  17. 2019RayNet: Learning Volumetric 3D Reconstruction with Ray Potentialscited by 26
  18. 2021Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibilitycited by 7
  19. 2016Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban Reconstructioncited by 6
  20. 2021Scalable Surface Reconstruction with Delaunay-Graph Neural Networkscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 1; 2026: 1.

8 papers.

  1. 2022ALSO: Automotive Lidar Self-supervision by Occupancy estimationcited by 68
  2. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
  3. 2023SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptationcited by 21
  4. 2025LiDPM: Rethinking Point Diffusion for Lidar Scene Completioncited by 10
  5. 2023SCoDA: Domain Adaptive Shape Completion for Real Scanscited by 9
  6. 2022DeepMend: Learning Occupancy Functions to Represent Shape for Repaircited by 6
  7. 2022DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repaircited by 5
  8. 2026SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completioncited by 0