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4 March 2022 TransGSnet: transformer-embedded ground segmentation of point cloud for rough roads
Dong He, Jong-Hwan Kim
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Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 1208409 (2022) https://doi.org/10.1117/12.2623141
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy
Abstract
Despite comprehensive works on point cloud ground segmentation for flat roads, research on rough roads has rarely been conducted due to dataset scarcity. To study point cloud ground segmentation on rough roads, in this paper, we provide a synthetic geometric transformation of flat roads motivated by the investigation of real-world rough roads. Our proposed TransGSnet framework consists of two modules: the pillar feature extractor, which turns a raw point cloud into the pseudo image as an intermediate representation, and the transformer-based segmentation network to perform ground segmentation. Specifically, our segmentation network exploits the U-Net architecture and includes three sub-modules: Transformer, mobile block (MB), and convolutional block attention module (CBAM). We thoroughly evaluate our framework in experiments, including comparisons against state-of-the-art approaches on semanticKITTI and the synthetic rough road dataset, respectively. As a result, our framework shows a great trade-off of performance cost.
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Dong He and Jong-Hwan Kim "TransGSnet: transformer-embedded ground segmentation of point cloud for rough roads", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 1208409 (4 March 2022); https://doi.org/10.1117/12.2623141
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KEYWORDS
Roads
Image segmentation
Transformers
Computer programming
LIDAR
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