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RVLoss: Runoff Vote Loss for Self-Supervised LiDAR Scene Flow Estimation — arXiv:2608.18864

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18864
License (as archived): CC0-1.0
Archived: 2026-10-18T14:06:29.108Z
SHA-256 of the archived text: c748d87f4e882705c555a54ab1269f06c9fee646765c0eb00d4e6bfa3636f5b7
Stable URL of this page: https://metraipolis.com/documento/dc_bb671fd4f2f0

How to cite

RVLoss: Runoff Vote Loss for Self-Supervised LiDAR Scene Flow Estimation — arXiv:2608.18864 — arXiv (en), https://arxiv.org/abs/2608.18864 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb671fd4f2f0 (Metraipolis archive dc_bb671fd4f2f0, sha256 c748d87f4e882705c555a54ab1269f06c9fee646765c0eb00d4e6bfa3636f5b7)

BibTeX

@misc{dcbb671fd4f2f0,
  title = {RVLoss: Runoff Vote Loss for Self-Supervised LiDAR Scene Flow Estimation — arXiv:2608.18864},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18864},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb671fd4f2f0; sha256 c748d87f4e882705c555a54ab1269f06c9fee646765c0eb00d4e6bfa3636f5b7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18864. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 the point-wise motion, calculated from nearest neighbor search, can often be grouped into a small set of dominant flow candidates by voting (top-k voting). Furthermore, when compensating the source by these candidates, the flow that best represents the underlying rigid motion often yields the highest consensus after a second voting (top-1 voting). Based on this insight, we incorporate the two-stage runoff vote into loss design and create cluster-wise rigid flows and free-form flows as pseudo-labels for self-supervised learning. RVLoss can be seamlessly integrated into existing feedforward architectures. Experiments on the Argoverse2 2026 Challenge show that models trained with RVLoss achieve state-of-the-art performance among self-supervised approaches, outperforming baseline models trained with alternative loss designs by 20%. Moreover, cross-dataset evaluations demonstrate consistent performance improvements across four additional datasets. Code will be released upon acceptance.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-20 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.18864 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T14:00:22.583Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).