FOSCo-Net: Ego-centric Voxel Completion for Forest LiDAR Occlusions
Overview
This paper introduces FOSCo-Net, a self-supervised voxel completion framework designed to address systematic LiDAR occlusions in temperate forests caused by tree trunks. The method reconstructs occluded geometry from a single scan using temporally accumulated observations as automatic supervision. The authors evaluate the framework using both cylindrical and Cartesian ego-centric grids with multiple decoder designs, finding that a gated skip decoder achieves the most reliable performance by significantly reducing false obstacle predictions compared to baseline methods.
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- arXiv Robotics — research abstractsSignalFOSCo-Net: Ego-centric Voxel Completion for Forest LiDAR Occlusions
This paper introduces FOSCo-Net, a self-supervised voxel completion framework designed to address systematic LiDAR occlusions in temperate forests caused by tree trunks. The method reconstructs occluded geometry from a single scan using temporally accumulated observations as automatic supervision. The authors evaluate the framework using both cylindrical and Cartesian ego-centric grids with multiple decoder designs, finding that a gated skip decoder achieves the most reliable performance by significantly reducing false obstacle predictions compared to baseline methods.
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