The DOI matches its Crossref record; 42 of the 45 references it cites were found in a registry or an index.
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”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [150]
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Cites
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpacePreprint
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 10,099
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R., Su, H., Mo, K., & Guibas, L. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.16 · Cited by 10,029
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Fan, H., Su, H., & Guibas, L. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.264 · Cited by 2,513
ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2015) · arXiv · doi:10.48550/arxiv.1512.03012 · Cited by 2,330
ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T. A., Guibas, L. J., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2023) · doi:10.5281/zenodo.10089705 · Cited by 1,774
Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y., & Ermon, S. (2019) · arXiv · doi:10.48550/arxiv.1907.05600 · Cited by 1,453
A Tutorial on Energy-Based Learning
LeCun, Y., Chopra, S., Hadsell, R. T., Ranzato, A., & Huang, F. J. (2006) · Cited by 844
(2005) · doi:10.5555/1046920.1088696 · Cited by 756
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Items 201–210 of 210
Geometry-Guided Diffusion SAR Point Cloud Denoising
Zhang, C., Jiang, T., Xu, X., Li, W., Zhang, F., & Chen, L. (2026) · Remote Sensing · doi:10.3390/rs18152458
PGAD: Progressive Geometry-Aware Denoising for Point Cloud Quality Inspection in Manufacturing
Zhao, J., Wang, W., Xu, Y., Jin, J., Zhang, C., Kong, C., Guo, B., Cao, Y., Jiang, X., & Pan, Y. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.03.132
LatentFlow3D: Unified Point Cloud Processing via Latent Space Modeling in Invertible Neural Networks
Mao, A., Yan, B., Yang, J., Duan, Y., Yi, R., Liu, Y., & He, Y. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02990-1
Ryu, J., Lee, J., Lee, J., & Oh, J. (2026) · Ocean Science Journal · doi:10.1007/s12601-026-00298-2
SA-P2SB: a structure-aware Schrödinger bridge for scene-level point cloud denoising
Zhang, Y., Zuo, T., & Wu, Z. (2026) · The Visual Computer · doi:10.1007/s00371-026-04670-0
Unraveling Noise in 3D Point Clouds: A Comprehensive Survey
Menéndez, A. G., Toribio, S. R. H., & González, R. C. (2026) · Metrology · doi:10.3390/metrology6040070
Deep Learning for Geometric 3D Point Cloud Enhancement: A Survey
Quan, S., Yu, J., Ziming, N., Wang, M., Feng, S., An, P., & Yang, J. (2026) · Computational Visual Media · doi:10.26599/cvm.2026.9450570
Fan, Q., & Gühmann, C. (2026) · tm - Technisches Messen · doi:10.1515/teme-2026-0075
Geometry-aware triplet learning for unsupervised point cloud denoising
Wang, W., Tong, S., & Wang, Y. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104812
Corrupted Point Cloud Completion Dataset(CPCCD)
Tesema, K. W. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.16085699
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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