AlignNet: spatiotemporal alignment and multi-scale feature fusion for enhanced LiDAR semantic segmentation

초록

Semantic segmentation is a critical task in LiDAR point cloud processing. Nevertheless, current approaches frequently rely too much on preceding frames, which might result in drift or cumulative mistakes due to unrestricted frame-by-frame stacking. This work offers a dynamic alignment of previous frame memory information with observations of the present frame. This ensures a more accurate capture of the features of the current frame and attempts to avoid errors due to changes in viewpoint or object motion. A new multi-scale feature fusion method was also shown. This method uses the spatiotemporal (ST) method to get the ST features, which lowers the differences between the 2D range image coordinates and the 3D Cartesian outputs. Through channel feature alignment and fusion, this method improves feature representation. SensatUrban, nuScenes, and SemanticKITTI datasets were used to test this approach. According to the experimental results, it performs more accurately than current state-of-the-art techniques.

키워드