RVLoss: Runoff Vote Loss for Self-Supervised LiDAR Scene Flow Estimation

RVLoss introduces a runoff vote mechanism for self‑supervised LiDAR scene flow estimation, addressing motion rigidity by grouping nearest‑neighbor derived motions into dominant flow candidates and selecting the most consistent one through a two‑stage voting process. This approach generates cluster‑wise rigid flows and free‑form flows as pseudo‑labels, enabling seamless integration into existing feedforward architectures. Experiments on the Argoverse2 2026 Challenge demonstrate that models trained with RVLoss outperform baseline self‑supervised methods by 20% and maintain consistent gains across four additional datasets.

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