Despite the importance of unsupervised object detection, to the best of our
knowledge, there is no previous work addressing this problem. One main issue,
widely known to the community, is that object boundaries derived only from 2D
image appearance are ambiguous and unreliable. To address this, we exploit
LiDAR clues to aid unsupervised object detection. By exploiting the 3D scene
structure, the issue of localization can be considerably mitigated. We further
identify another major issue, seldom noticed by the community, that the
long-tailed and open-ended (sub-)category distribution should be accommodated.
In this paper, we present the first practical method for unsupervised object
detection with the aid of LiDAR clues. In our approach, candidate object
segments based on 3D point clouds are firstly generated. Then, an iterative
segment labeling process is conducted to assign segment labels and to train a
segment labeling network, which is based on features from both 2D images and 3D
point clouds. The labeling process is carefully designed so as to mitigate the
issue of long-tailed and open-ended distribution. The final segment labels are
set as pseudo annotations for object detection network training. Extensive
experiments on the large-scale Waymo Open dataset suggest that the derived
unsupervised object detection method achieves reasonable accuracy compared with
that of strong supervision within the LiDAR visible range. Code shall be
released.

通过利用 LiDAR 数据辅助进行无监督的二维对象检测，同时采用基于 3D 点云和 2D 图像特征的迭代分割标签网络对候选对象进行标签生成和训练，从而解决了之前存在的问题，得到了较为合理的检测结果。