mlsanityData to 14 Oct 2026

Neural Stochastic Screened Poisson Reconstruction

arXiv 2309.11993 ↗First posted 21 Sep 2023cs.GR cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

Reconstructing a surface from a point cloud is an underdetermined problem. We use a neural network to study and quantify this reconstruction uncertainty under a Poisson smoothness prior. Our algorithm addresses the main limitations of existing work and can be fully integrated into the 3D scanning pipeline, from obtaining an initial reconstruction to deciding on the next best sensor position and updating the reconstruction upon capturing more data.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 20 of its 52 references that are in the map. First posted on arXiv 21 Sep 2023.

Builds on

20 of 52 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  5. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  6. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  7. 2017The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problemscited by 583
  8. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  9. 2021iMAP: Implicit Mapping and Positioning in Real-Timecited by 476
  10. 2020STAR: Sparse Trained Articulated Human Body Regressorcited by 194
  11. 2017A Survey of Structure from Motioncited by 158
  12. 2018Deep Geometric Prior for Surface Reconstructioncited by 110
8 more
  1. 2023vMAP: Vectorised Object Mapping for Neural Field SLAMcited by 67
  2. 2023NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Renderingcited by 54
  3. 2021Orienting Point Clouds with Dipole Propagationcited by 31
  4. 2015Differentiating the multipoint Expected Improvement for optimal batch designcited by 13
  5. 2022Stochastic Poisson Surface Reconstructioncited by 13
  6. 2020Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximizationcited by 9
  7. 2022Uncertainty-Driven Active Vision for Implicit Scene Reconstructioncited by 6
  8. 2017Surface Reconstruction with Data-driven Exemplar Priorscited by 3

Built on by

Cited by year (2026 to 14 Oct)

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

7 papers.

  1. 2023Objects as volumes: A stochastic geometry view of opaque solidscited by 15
  2. 2024Geometry-Informed Neural Networkscited by 12
  3. 2024Consistent Point Orientation for Manifold Surfaces via Boundary Integrationcited by 4
  4. 2025U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matchingcited by 3
  5. 2025Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processescited by 2
  6. 2026Variational Green's Functions for Volumetric PDEscited by 1
  7. 2026Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methodscited by 0