标题： UniLiPs：基于几何基础的动态场景分解的统一激光雷达伪标签方法 显示英文标题

标题： UniLiPs: Unified LiDAR Pseudo-Labeling with Geometry-Grounded Dynamic Scene Decomposition

摘要： 无标签的LiDAR日志，在自动驾驶应用中，本质上是隐藏在显而易见之处的密集3D几何信息的金矿 - 但如果没有人工标签，它们几乎毫无用处，这突显了自动驾驶感知研究中的主要成本障碍。 在这项工作中，我们通过利用LiDAR扫描之间的时序几何一致性，将文本和2D视觉基础模型的提示直接提升并融合到3D中，而无需任何手动输入。 我们引入了一种无监督的多模态伪标签方法，该方法依赖于从时间累积的LiDAR地图中学习到的强几何先验，同时引入了一种新颖的迭代更新规则，以强制执行联合几何-语义一致性，并反之通过不一致性检测移动物体。 我们的方法同时生成3D语义标签、3D边界框和密集的LiDAR扫描，展示了在三个数据集上的强大泛化能力。 我们通过实验验证了我们的方法在现有语义分割和目标检测伪标签方法中表现优异，这些方法通常需要额外的人工监督。 我们确认，我们几何一致的、密度增加的LiDAR即使只占很小一部分，也能分别在80-150米和150-250米范围内将深度预测提高51.5%和22.0%的MAE。 显示英文摘要

摘要： Unlabeled LiDAR logs, in autonomous driving applications, are inherently a gold mine of dense 3D geometry hiding in plain sight - yet they are almost useless without human labels, highlighting a dominant cost barrier for autonomous-perception research. In this work we tackle this bottleneck by leveraging temporal-geometric consistency across LiDAR sweeps to lift and fuse cues from text and 2D vision foundation models directly into 3D, without any manual input. We introduce an unsupervised multi-modal pseudo-labeling method relying on strong geometric priors learned from temporally accumulated LiDAR maps, alongside with a novel iterative update rule that enforces joint geometric-semantic consistency, and vice-versa detecting moving objects from inconsistencies. Our method simultaneously produces 3D semantic labels, 3D bounding boxes, and dense LiDAR scans, demonstrating robust generalization across three datasets. We experimentally validate that our method compares favorably to existing semantic segmentation and object detection pseudo-labeling methods, which often require additional manual supervision. We confirm that even a small fraction of our geometrically consistent, densified LiDAR improves depth prediction by 51.5% and 22.0% MAE in the 80-150 and 150-250 meters range, respectively.