mlsanityData to 14 Oct 2026

Gradient-based Point Cloud Denoising with Uniformity

arXiv 2207.10279 ↗First posted 21 Jul 2022cs.CV cs.GRcited by 1

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Abstract (arXiv)

Point clouds captured by depth sensors are often contaminated by noises, obstructing further analysis and applications. In this paper, we emphasize the importance of point distribution uniformity to downstream tasks. We demonstrate that point clouds produced by existing gradient-based denoisers lack uniformity despite having achieved promising quantitative results. To this end, we propose GPCD++, a gradient-based denoiser with an ultra-lightweight network named UniNet to address uniformity. Compared with previous state-of-the-art methods, our approach not only generates competitive or even better denoising results, but also significantly improves uniformity which largely benefits applications such as surface reconstruction.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 21 Jul 2022.

Builds on

22 of 40 references.

References · 22

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2018PU-Net: Point Cloud Upsampling Networkcited by 209
  5. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  6. 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 164
  7. 2018Patch-based Progressive 3D Point Set Upsamplingcited by 101
  8. 2019PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Cloudscited by 99
  9. 2021Score-Based Point Cloud Denoisingcited by 83
  10. 2021Point Cloud Upsampling via Disentangled Refinementcited by 74
  11. 2019Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaningcited by 58
  12. 2020Differentiable Manifold Reconstruction for Point Cloud Denoisingcited by 52
10 more
  1. 2021ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentationcited by 52
  2. 20183D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Modelcited by 47
  3. 2020Learning Graph-Convolutional Representations for Point Cloud Denoisingcited by 40
  4. 2021Deep Point Set Resampling via Gradient Fieldscited by 24
  5. 2021Learning with Noisy Labels for Robust Point Cloud Segmentationcited by 22
  6. 20193D Point Cloud Denoising via Deep Neural Network based Local Surface Estimationcited by 20
  7. 2020Dynamic Point Cloud Denoising via Manifold-to-Manifold Distancecited by 12
  8. 2016Density-based Denoising of Point Cloudcited by 11
  9. 2021Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstructioncited by 7
  10. 2017Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversitycited by 6

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Deep Learning For Point Cloud Denoising: A Surveycited by 1