Abstract
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.
Abstract (translated)
在自动驾驶应用中，未标注的LiDAR日志本质上隐藏着丰富的3D几何信息——然而，在没有人工标签的情况下，这些数据几乎无法使用，这突出显示了自主感知研究中的主要成本障碍。在这项工作中，我们通过利用跨时间的几何一致性来解决这一瓶颈问题，直接将文本和2D视觉基础模型中的线索提升并融合到3D空间中，无需任何手动输入。我们介绍了一种无监督的多模态伪标签生成方法，该方法依赖于从累积的时间LiDAR地图中学得的强大几何先验，并且引入了一个新颖的迭代更新规则，以强制执行几何语义的一致性，反之亦可检测移动物体中的不一致之处。 我们的方法同时产生3D语义标签、3D边界框以及密集的LiDAR扫描数据，在三个不同的数据集上展示了稳健的泛化能力。我们通过实验验证了该方法与现有的语义分割和目标检测伪标签生成方法相比具有优势，而这些现有方法往往需要额外的手动监督。 我们确认，即使少量我们的几何一致、密度增强的LiDAR数据也能使深度预测在80-150米范围内的MAE误差降低51.5%，以及在150-250米范围内的MAE误差降低22%。
URL
https://arxiv.org/abs/2601.05105
