LiDAR-SGMOS: Semantics-Guided Moving Object Segmentation with 3D LiDAR

S Gu, S Yao, J Yang, C Xu… - 2023 IEEE/RSJ …, 2023 - ieeexplore.ieee.org
2023 IEEE/RSJ International Conference on Intelligent Robots and …, 2023ieeexplore.ieee.org
Most of the existing moving object segmentation (MOS) methods regard MOS as an
independent task, in this paper, we associate the MOS task with semantic segmentation, and
propose a semantics-guided network for moving object segmentation (LiDAR-SGMOS). We
first transform the range image and semantic features of the past scan into the range view of
current scan based on the relative pose between scans. The residual image is obtained by
calculating the normalized absolute difference between the current and transformed range …
Most of the existing moving object segmentation (MOS) methods regard MOS as an independent task, in this paper, we associate the MOS task with semantic segmentation, and propose a semantics-guided network for moving object segmentation (LiDAR-SGMOS). We first transform the range image and semantic features of the past scan into the range view of current scan based on the relative pose between scans. The residual image is obtained by calculating the normalized absolute difference between the current and transformed range images. Then, we apply a Meta-Kernel based cross scan fusion (CSF) module to adaptively fuse the range images and semantic features of current scan, the residual image and transformed features. Finally, the fused features with rich motion and semantic information are processed to obtain reliable MOS results. We also introduce a residual image augmentation method to further improve the MOS performance. Our method outperforms most LiDAR-MOS methods with only two sequential LiDAR scans as inputs on the SemanticKITTI MOS dataset.
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