1D-SalsaSAN: Semantic Segmentation of LiDAR Point Cloud with Self-Attention
- Takahiro Suzuki, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
- International Conference on Computer Vision Theory and Applications, 2023
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Semantic segmentation on the three-dimensional (3D) point-cloud data acquired from omnidirectional light detection and ranging (LiDAR) identifies static objects, such as roads, and dynamic objects such as vehicles and pedestrians. This enables us to recognize the environment in all directions around a vehicle, which is necessary for autonomous driving. Processing such data requires a huge amount of computation. Therefore, methods have been proposed for converting 3D point-cloud data into pseudo-images and executing semantic segmentation to increase the processing speed. With these methods, a large amount of point-cloud data are lost when converting 3D point-cloud data to pseudo-images, which tends to decrease the identification accuracy of small objects such as pedestrians and traffic signs with a small number of pixels. We propose a semantic segmentation method that involves projection using Scan-Unfolding and a 1D self-attention block that is on the basis of the self-attention block. As a result of an evaluation using SemanticKITTI, we confirmed that the proposed method improves the accuracy of semantic segmentation, contributes to the improvement of small object identification accuracy, and is sufficient regarding processing speed. We also showed that the proposed method is fast enough for real-time processing.