Score-Based Point Cloud Denoising
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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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Diffusion models have shown potential in computer vision, natural language processing, temporal data modeling, multi-modal modeling, robust machine learning, computational chemistry, and medical image reconstruction.
“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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RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments
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Fast Learning of Signed Distance Functions From Noisy Point Clouds via Noise to Noise Mapping
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Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
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Progressive Growth for Point Cloud Completion by Surface-Projection Optimization
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From Noise Addition to Denoising: A Self-Variation Capture Network for Point Cloud Optimization
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Wiener Filter-Based Point Cloud Adaptive Denoising for Video-based Point Cloud Compression
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A point cloud denoising network based on manifold in an unknown noisy environment
Li, Z., Pan, W., Wang, S., Tang, X., & Hu, H. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.104735 · Cited by 11 · see citing works
Deterministic Point Cloud Diffusion for Denoising
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FLAT: Flux-Aware Imperceptible Adversarial Attacks on 3D Point Clouds
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Diffusion Models and Generative Artificial Intelligence: Frameworks, Applications and Challenges
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3D Object Detection From Point Cloud via Voting Step Diffusion
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Learning Gradient Fields for Scalable and Generalizable Irregular Packing
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TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards Upsampling
Kumbar, A., Anvekar, T., Vikrama, T. A., Tabib, R. A., & Mudenagudi, U. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00241 · Cited by 8 · see citing works
Learning Implicit Fields for Point Cloud Filtering
Wang, J., Lu, X., Wang, M., Hou, F., & He, Y. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3450699 · Cited by 8 · see citing works
SITF: A Self-Supervised Iterative Training Framework for Point Cloud Denoising
Su, Z., Wang, C., Jiang, K., Jiang, K., & Li, W. (2024) · Computer-Aided Design · doi:10.1016/j.cad.2024.103812 · Cited by 8 · see citing works
PyramidPCD: A novel pyramid network for point cloud denoising
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Efficient Deep Super-Resolution of Voxelized Point Cloud in Geometry Compression
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Deep learning-based point cloud upsampling: a review of recent trends
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ReVISOR: ResUNets with visibility and intensity for structured outlier removal
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PN-Internet: Point-and-Normal Interactive Network for Noisy Point Clouds
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Time Matters: Enhancing Sequential Recommendations with Time-Guided Graph Neural ODEs
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Enhancing LiDAR-Based Object Recognition Through a Novel Denoising and Modified GDANet Framework
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Efficient Federated Learning With Quality-Aware Generated Models: An Incentive Mechanism
Zhang, H., Li, P., Dai, M., Wu, Y., & Qian, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3461329 · Cited by 6 · see citing works
Deep non-local point cloud denoising network
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Denoising-While-Completing Network (DWCNet): Robust point cloud completion under corruption
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Generative AI for 3D Molecular Structure Prediction Using Diffusion Models
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TBFNT3D: Two-Branch Fusion Network With Transformer for Multimodal Indoor 3D Object Detection
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Denoising point clouds with fewer learnable parameters
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Digital reconstruction of railway steep slope from UAV+TLS using geometric transformer
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Nonparametric point cloud filter
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Point cloud denoising using a generalized error metric
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Denoise yourself: Self-supervised point cloud upsampling with pretrained denoising
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De(l)Noise: A Delaunay based point cloud denoising algorithm
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Pre-training meets iteration: Learning for robust 3D point cloud denoising
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Pagoda: Privacy Protection for Volumetric Video Streaming through Poisson Diffusion Model
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Multi-Layered Surface Estimation for Low-Cost Optical Coherence Tomography
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- 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 › Engineering › Computational Mechanics › 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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