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Dynamic Graph CNN for Learning on Point Clouds
Open accessAbstract
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.
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“Our method instead learns edge feature representations within both mesh and geodesic neighborhoods, and combines them into vertex representations inspired by the edge convolution scheme of [Wang et al. 2019].”
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VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion
Stier, N., Rich, A., Sen, P., & Höllerer, T. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00042 · Cited by 57
FPCC: Fast point cloud clustering-based instance segmentation for industrial bin-picking
Xu, Y., Arai, S., Liu, D., Lin, F., & Kosuge, K. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.023 · Cited by 56
Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks
Pan, X., Mai, J., Jiang, X., Tang, D., Li, J., Shao, T., Zhou, K., Jin, X., & Manocha, D. (2022) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3528233.3530709 · Cited by 52
Graph neural network-enabled manufacturing method classification from engineering drawings
Xie, L., Lu, Y., Furuhata, T., Yamakawa, S., Zhang, W., Regmi, A., Kara, L. B., & Shimada, K. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103697 · Cited by 52
Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization
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DrapeNet: Garment Generation and Self-Supervised Draping
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Self-supervised learning for point cloud data: A survey
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Deep learning based 3D segmentation in computer vision: A survey
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Fan, Y., Lam, J. C. K., & Li, V. O. K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6963 · Cited by 52
Liu, Q., Kampffmeyer, M., Jenssen, R., & Salberg, A.-B. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00030 · Cited by 52
3D-Rotation-Equivariant Quaternion Neural Networks
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Learning with Noisy Labels for Robust Point Cloud Segmentation
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PointRGCN: Graph Convolution Networks for 3D Vehicles Detection RefinementPreprint
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Neuromorphic Camera Denoising Using Graph Neural Network-Driven Transformers
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Metadata
- Authors
- Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M.
- Affiliations
- Massachusetts Institute of Technology (US)
- International Computer Science Institute (US)
- Imperial College London (GB)
- Venue
- ACM Transactions on Graphics
- Published
- 2019-10-10
- Volume
- 38
- Issue
- 5
- Pages
- 1-12
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2979750740
- Semantic Scholar
- e1799aaf23c12af6932dc0ef3dfb1638f01413d1
- Cited by
- 7,199
- References
- 90
- Citations by year
- 2026: 865 · 2025: 1221 · 2024: 1301 · 2023: 1160 · 2022: 924 · 2021: 887 · 2020: 570 · 2019: 233
- FWCI
- 443.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Graphics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Air Force Office of Scientific Research (FA9550-19-1-0319)
- Toyota-CSAIL Joint Research Center
- National Science Foundation (IIS-1838071)
- Amazon Research Award
- MITIBM Watson AI Laboratory
- Skoltech-MIT Next Generation Program
- Army Research Office (W911NF-12-R-0011)
- ERC Consolidator (724228 (LEMAN))
- Google Faculty Research Award
- 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 › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- point clouds, dynamic graph convolution, graph neural networks, 3D shape classification, 3D shape segmentation, point cloud topology, local neighborhood information, 3D object recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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