# dgcnn **Repository Path**: away053/dgcnn ## Basic Information - **Project Name**: dgcnn - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-12-03 - **Last Updated**: 2024-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Dynamic Graph CNN for Learning on Point Clouds We propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv is differentiable and can be plugged into existing architectures. [[Project]](https://liuziwei7.github.io/projects/DGCNN) [[Paper]](https://arxiv.org/abs/1801.07829) ## Overview `DGCNN` is the author's re-implementation of Dynamic Graph CNN, which achieves state-of-the-art performance on point-cloud-related high-level tasks including category classification, semantic segmentation and part segmentation. Further information please contact [Yue Wang](https://www.csail.mit.edu/person/yue-wang) and [Yongbin Sun](https://autoid.mit.edu/people-2). ## Author's Implementations The classification experiments in our paper are done with the pytorch implementation. * [tensorflow-dgcnn](./tensorflow) * [pytorch-dgcnn](./pytorch) ## Other Implementations * [pytorch-geometric](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.EdgeConv) ## Citation Please cite this paper if you want to use it in your work, @article{dgcnn, title={Dynamic Graph CNN for Learning on Point Clouds}, author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.}, journal={ACM Transactions on Graphics (TOG)}, year={2019} } ## License MIT License ## Acknowledgement The structure of this codebase is borrowed from [PointNet](https://github.com/charlesq34/pointnet).