发表机构
TU Delft; University of Nottingham（代尔夫特理工大学; 诺丁汉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对自监督激光雷达场景流估计中运动刚性不足的问题，提出融入 runoff vote 机制的 RVLoss，在 Argoverse2 2026 挑战赛上性能超基线 20%，跨数据集也有稳定提升。
AI 中文摘要
激光雷达场景流估计两个连续扫描帧（源帧和目标帧）之间逐点的运动。主流自监督方法通常最小化 Chamfer 损失，即流补偿后的源帧与目标帧之间的最近邻距离。然而，最近邻搜索无法强制运动刚性，常导致同一物体实例内的流不一致。现有方法通过额外正则化项解决该问题，但点间流一致性仍有限，尤其对于大物体。我们提出 RVLoss，一种通过 runoff vote 机制设计融入运动刚性的自监督损失。我们的关键观察是，由最近邻搜索计算的逐点运动，可通过投票（top-k 投票）分组为少量主导流候选；此外，当用这些候选补偿源帧时，最能代表潜在刚性运动的流会在第二次投票（top-1 投票）后产生最高共识。基于此见解，我们将两阶段 runoff vote 融入损失设计，并创建簇级刚性流和自由形式流作为自监督学习的伪标签。RVLoss 可无缝集成到现有前馈架构中。在 Argoverse2 2026 挑战赛上的实验表明，使用 RVLoss 训练的模型在自监督方法中达到了最优性能，比使用其他损失设计训练的基线模型表现高出 20%。此外，跨数据集评估在四个额外数据集上均显示出一致的性能提升。代码将在论文接收后发布。
英文摘要
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.
Comments15 pages, 5 figures
