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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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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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Zhou, Z., Jia, Y., Zhu, T., & Wan, Y. (2025) · Information · doi:10.3390/info16030176 · Cited by 3
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Li, W., Yang, Y., Zhang, W., Gao, Y., & Jin, Z. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3647068 · Cited by 2
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Ding, J., Chen, H., Zhou, J., Wu, D., Chen, X., & Wang, L. (2022) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc55534.2022.00052 · Cited by 2
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Learning-based geometric framework for noise discernment and denoising of 2D point sets
Reghunath, M., Ananth, K., Antony, J., Muralidharan, K., & Muthuganapathy, R. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104327 · Cited by 2
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3D Point Cloud Denoising Based on Hybrid Attention Mechanism and Score Matching
Wang, Z., Sun, W., & Tian, L. (2022) · doi:10.1145/3573942.3574093 · Cited by 1
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Barsanti, S. G., Marini, M. R., Malatesta, S. G., & Rossi, A. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-71013-1_10 · Cited by 1
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Xiong, B. (2023) · Journal of Electronics and Information Science · doi:10.23977/jeis.2023.080309
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Ma, Y., Yue, Z., Wang, Y., Liu, R., Su, Z., & Cao, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0376-0_22
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Joint neural denoising and consolidation for portable handheld laser scan
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Point cloud processing using non-volatile memories with circuit and sensor noise
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Multi-Head Self-Attention Enhanced Score Network for Point Cloud Denoising
Jie, Z., Yin, P., Xu, F., & Zhang, X. (2025) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl65390.2025.11085822
Kanfilter: a simple and effective multi-module point cloud denoising model
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Zuo, L., Zhang, J., Ding, S., Cai, H., & Lv, Y. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163859
Point Cloud Denoising with Graphtransformerblock and Dual-Stream Scorenet
Chen, J., Wang, Q., & Wang, T. (2025) · International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) · doi:10.1109/aiea66061.2025.11160572
Li, Q., Jiang, Y., Cheng, J., Chen, W., Zhao, P., Qiao, X., Cheng, C., & Yuan, Y. (2025) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim64088.2025.11175744
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Marin, D., Ilic, F., Ohrhallinger, S., & Wimmer, M. H. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-07623-6_4
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Li, J., Li, M., Yang, G., Tang, D., Hu, D., & Jiang, C. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3679497
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Ji, W., Miao, Z., Li, Y., Wang, J., & Lu, X. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3685878
RadTwin: Generalizable Wireless Digital Twin for Dynamic Environments
Zhang, Y., Zhao, M., Liu, Q., Alkhateeb, A., Agrawal, A., & Qu, Q. (2026) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn69946.2026.11662634
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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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