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Open AccessArticle

Spatial Aggregation Net: Point Cloud Semantic Segmentation Based on Multi-Directional Convolution

1
Computer Engineering College, Jimei University, Xiamen 361021, China
2
Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(19), 4329; https://doi.org/10.3390/s19194329
Received: 31 July 2019 / Revised: 29 September 2019 / Accepted: 2 October 2019 / Published: 7 October 2019
(This article belongs to the Special Issue Mobile Laser Scanning Systems)
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. View Full-Text
Keywords: LiDAR point cloud; deep learning; semantic segmentation; spatial structure information LiDAR point cloud; deep learning; semantic segmentation; spatial structure information
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MDPI and ACS Style

Cai, G.; Jiang, Z.; Wang, Z.; Huang, S.; Chen, K.; Ge, X.; Wu, Y. Spatial Aggregation Net: Point Cloud Semantic Segmentation Based on Multi-Directional Convolution. Sensors 2019, 19, 4329.

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