Hierarchical depthwise graph convolutional neural network for 3D semantic segmentation of point clouds

Hierarchical depthwise graph convolutional neural network for 3D semantic segmentation of point clouds. Liang, Z., Yang, M., Deng, L., Wang, C., & Wang, B. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May:8152-8158, 2019.

Paper doi abstract bibtex

This paper proposes a hierarchical depthwise graph convolutional neural network (HDGCN) for point cloud semantic segmentation. The main chanllenge for learning on point clouds is to capture local structures or relationships. Graph convolution has the strong ability to extract local shape information from neighbors. Inspired by depthwise convolution, we propose a depthwise graph convolution which requires less memory consumption compared with the previous graph convolution. While depthwise graph convolution aggregates features channel-wisely, pointwise convolution is used to learn features across different channels. A customized block called DGConv is specially designed for local feature extraction based on depthwise graph convolution and pointwise convolution. The DGConv block can extract features from points and transfer features to neighbors while being invariant to different point orders. HDGCN is constructed by a series of DGConv blocks using a hierarchical structure which can extract both local and global features of point clouds. Experiments show that HDGCN achieves the state-of-the-art performance in the indoor dataset S3DIS and the outdoor dataset Paris-Lille-3D.

@article{
 title = {Hierarchical depthwise graph convolutional neural network for 3D semantic segmentation of point clouds},
 type = {article},
 year = {2019},
 pages = {8152-8158},
 volume = {2019-May},
 id = {e53cf6d7-8a62-3d8f-93f7-2ad3d79d57bd},
 created = {2021-08-04T13:05:08.171Z},
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 last_modified = {2021-10-13T14:40:20.458Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
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 abstract = {This paper proposes a hierarchical depthwise graph convolutional neural network (HDGCN) for point cloud semantic segmentation. The main chanllenge for learning on point clouds is to capture local structures or relationships. Graph convolution has the strong ability to extract local shape information from neighbors. Inspired by depthwise convolution, we propose a depthwise graph convolution which requires less memory consumption compared with the previous graph convolution. While depthwise graph convolution aggregates features channel-wisely, pointwise convolution is used to learn features across different channels. A customized block called DGConv is specially designed for local feature extraction based on depthwise graph convolution and pointwise convolution. The DGConv block can extract features from points and transfer features to neighbors while being invariant to different point orders. HDGCN is constructed by a series of DGConv blocks using a hierarchical structure which can extract both local and global features of point clouds. Experiments show that HDGCN achieves the state-of-the-art performance in the indoor dataset S3DIS and the outdoor dataset Paris-Lille-3D.},
 bibtype = {article},
 author = {Liang, Zhidong and Yang, Ming and Deng, Liuyuan and Wang, Chunxiang and Wang, Bing},
 doi = {10.1109/ICRA.2019.8794052},
 journal = {Proceedings - IEEE International Conference on Robotics and Automation}
}

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