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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].”
From RigNet: Neural Rigging for Articulated Characters (2020) · cites this paper as Wang et al. 2019
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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
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