RVLoss: Runoff Vote Loss for Self-Supervised LiDAR Scene Flow Estimation
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 motion rigidity, often leading to inconsistent flows within object instances. Existing approaches address this issue with additional regularization terms, but flow consistency among points remains limited, especially for large objects. We propose RVLoss, a self-supervised loss that incorporates motion rigidity by design through a runoff vote mechanism. Our key observation is that...
| Authors | Shiming Wang, Liangliang Nan, Julian Kooij et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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