Online object segmentation and tracking in Lidar point clouds enables autonomous agents to understand their surroundings and make safe decisions. Unfortunately, manual annotations for these tasks are prohibitively costly. We tackle this problem with the task of class-agnostic unsupervised online instance segmentation and tracking. To that end, we leverage an instance segmentation backbone and propose a new training recipe that enables the online tracking of objects. Our network is trained on pseudo-labels, eliminating the need for manual annotations. We conduct an evaluation using metrics adapted for temporal instance segmentation. Computing these metrics requires temporally-consistent instance labels. When unavailable, we construct these labels using the available 3D bounding boxes and semantic labels in the dataset. We compare our method against strong baselines and demonstrate its superiority across two different outdoor Lidar datasets.

本研究解决了在激光雷达点云中进行在线目标分割和跟踪时缺乏手动标注的问题。我们提出了一种新的训练方案，允许在无监督的条件下实现目标的实例分割和追踪，利用伪标签进行训练。该方法在两个户外激光雷达数据集上表现出优越性，具有重要的实际应用潜力。

UNIT：通过时间的无监督在线实例分割