arXiv:2611.06588 · v1
FOSCo-Net: Ego-centric Voxel Completion for Forest LiDAR Occlusions
摘要
Ground robots operating in temperate forests suffer from systematic LiDAR occlusions caused by tree trunks, creating persistent blind regions that degrade traversability estimation. In this paper, we propose FOSCo-Net, a self-supervised voxel completion framework that learns to reconstruct occluded geometry from a single scan using temporally accumulated observations as automatic supervision, and we evaluate cylindrical and Cartesian ego-centric grids with multiple decoder designs. FOSCo-Net consistently improves reconstruction quality and traversal safety, with a gated skip decoder achieving the most reliable performance and significantly reducing false obstacle predictions compared to baselines.
作者备注：12 pages, 6 figures, accepted for presentation at ACIVS 2026 (23rd Advanced Concepts for Intelligent Vision Systems) Okinawa, Japan