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Score-Based Point Cloud Denoising
Open accessAbstract
Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. The distribution of a noisy point cloud can be viewed as the distribution of a set of noise-free samples p(x) convolved with some noise model n, leading to (p * n)(x) whose mode is the underlying clean surface. To denoise a noisy point cloud, we propose to increase the log-likelihood of each point from p * n via gradient ascent—iteratively updating each point’s position. Since p * n is unknown at test-time, and we only need the score (i.e., the gradient of the log-probability function) to perform gradient ascent, we propose a neural network architecture to estimate the score of p * n given only noisy point clouds as input. We derive objective functions for training the network and develop a denoising algorithm leveraging on the estimated scores. Experiments demonstrate that the proposed model outperforms state-of-the-art methods under a variety of noise models, and shows the potential to be applied in other tasks such as point cloud upsampling.
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“and have also shown potential in a variety of domains, ranging from computer vision [2,11,19,23,88,90,113,115,134,150,160,173,198,200,248,270,271,283,290], natural language processing [6,93,139,205,275], temporal data modeling [1,33,124,191,233,262], multi-modal modeling [7,186,196,199,288], robust machine learning [18,28,112,242,273], to interdisciplinary applications in fields such as computa”
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Self-citationZhang, C., Zhang, X., Yue, X., Lao, M., Jiang, T., Wang, J., Zhang, F., & Chen, L. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681540 · Cited by 6
Nonparametric point cloud filter
Sun, Y., & Wang, J. (2023) · IET Image Processing · doi:10.1049/ipr2.12955 · Cited by 6
Zhang, J., Zhang, J., Ding, D., & Ma, Z. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3375861 · Cited by 6
Point cloud denoising using a generalized error metric
Xu, Q., Yang, Y., & Deng, B. (2024) · Graphical Models · doi:10.1016/j.gmod.2024.101216 · Cited by 5
Denoise yourself: Self-supervised point cloud upsampling with pretrained denoising
Hur, J.-H., Kwon, S., & Kim, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126638 · Cited by 5
Joint state-parameter estimation for the reduced fracture model via the united filter
Huynh, P. T., Hoang, T., Zhang, G., & Bao, F. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.114159 · Cited by 6
De(l)Noise: A Delaunay based point cloud denoising algorithm
Reghunath, M., Muralidharan, K., Gurumoorthi, A. R., Ananth, K., Sureshkumar, H. V., Thayyil, S. B., & Muthuganapathy, R. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104310 · Cited by 5
EVALUATION OF A SLAM-BASED POINT CLOUD FOR DEFLECTION ANALYSIS IN HISTORIC TIMBER FLOORS
Sánchez-Aparicio, L. J., Llauradó, P. V., Sanz-Honrado, P., Aira, J. R., Pinilla, J., González‐Aguilera, D., & Oliveira, D. V. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-m-2-2023-1411-2023 · Cited by 5
Chen, W., Fan, H., Jiang, Q., Huang, C., & Yang, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680687 · Cited by 5
3D point cloud denoising via Gaussian processes regression
Kim, I., & Singh, S. K. (2025) · The Visual Computer · doi:10.1007/s00371-025-04049-7 · Cited by 4
PU-MG: Mutual guidance framework for Point Cloud Upsampling
Tang, X.-P., Shao, F., Mu, B., & Chen, H. (2025) · Displays · doi:10.1016/j.displa.2025.103270 · Cited by 4
Deep learning-based quality enhancement for 3D point clouds：a survey
Jianwen, C., Zhao, L., Ren, L., Zhuoqun, S., Xinfeng, Z., & Siwei, M. (2023) · Journal of Image and Graphics · doi:10.11834/jig.221076 · Cited by 4
Pre-training meets iteration: Learning for robust 3D point cloud denoising
Quan, S., Zhao, H., Zhao, Z., Nie, Z., & Yang, J. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.02.012 · Cited by 4
3D-NLM: Voxel-based non-local means for 3D point cloud noise detection and smoothing
Wang, W., Tan, X., Li, L., Liu, Y., & Chang, Q. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104348 · Cited by 4
Pagoda: Privacy Protection for Volumetric Video Streaming through Poisson Diffusion Model
Lu, R., Wei, L., Zhu, S., Hu, C., & Wang, D. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611946 · Cited by 4
Multi-Layered Surface Estimation for Low-Cost Optical Coherence Tomography
Rapp, J., Mansour, H., Boufounos, P. T., Koike–Akino, T., & Parsons, K. (2024) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2024.3497602 · Cited by 4
Metadata
- Authors
- Luo, S., & Hu, W.
- Affiliations
- Peking University (CN)
- ORCID
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 4563-4572
- OpenAlex
- W3183474792
- Cited by
- 209
- References
- 45
- Citations by year
- 2026: 33 · 2025: 61 · 2024: 51 · 2023: 47 · 2022: 17
- FWCI
- 21.14
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- point cloud denoising, score-based generative models, score estimation, gradient ascent, noise models, log-likelihood, point cloud upsampling, surface reconstruction
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