LiDAR scene flow estimates point-wise motion between two consecutive scans, referred to as the source and target. Leading self-supervised methods typically minimize the Chamfer loss, the nearest neighbor distance between the flow-compensated source and the target. However, nearest-neighbor search does not enforce motio...
Shiming Wang, Liang-Liang Nan, Julian F. P. Kooij et al.· 1 citation
Experiments with a state-of-the-art CVL model on OpenCVL show that incorporating noisy in-the-wild data consistently improves performance on clean test sets, suggesting a promising direction for scaling CVL with diverse real-world imagery.
Zi-Min Xia, Mubariz Zaffar, Junfan Fu et al.· 0 citations
This work investigates whether a frozen, self-supervised point transformer already contains the structural information required to isolate object instances without any handcrafted geometric prior, and develops a training-free segmenter that groups points via connected components on a key-similarity graph, using neither...
Ted Lentsch, Santiago Montiel-Mar'in, Holger Caesar et al.· 0 citations
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