Sensors, Vol. 19, Pages 4329: Spatial Aggregation Net: Point Cloud Semantic Segmentation Based on Multi-Directional Convolution
Sensors, Vol. 19, Pages 4329: Spatial Aggregation Net: Point Cloud Semantic Segmentation Based on Multi-Directional Convolution
Sensors doi: 10.3390/s19194329
Authors:
Guorong Cai
Zuning Jiang
Zongyue Wang
Shangfeng Huang
Kai Chen
Xuyang Ge
Yundong Wu
Semantic segmentation of 3D point clouds plays a vital role in autonomous driving, 3D maps, and smart cities, etc. Recent work such as PointSIFT shows that spatial structure information can improve the performance of semantic segmentation. Motivated by this phenomenon, we propose Spatial Aggregation Net (SAN) for point cloud semantic segmentation. SAN is based on multi-directional convolution scheme that utilizes the spatial structure information of point cloud. Firstly, Octant-Search is employed to capture the neighboring points around each sampled point. Secondly, we use multi-directional convolution to extract information from different directions of sampled points. Finally, max-pooling is used to aggregate information from different directions. The experimental results conducted on ScanNet database show that the proposed SAN has comparable results with state-of-the-art algorithms such as PointNet, PointNet++, and PointSIFT, etc. In particular, our method has better performance on flat, small objects, and the edge areas that connect objects. Moreover, our model has good trade-off in segmentation accuracy and time complexity.
Source: Sensors - Category: Biotechnology Authors: Guorong Cai Zuning Jiang Zongyue Wang Shangfeng Huang Kai Chen Xuyang Ge Yundong Wu Tags: Article Source Type: research