  Unsupervised 3D object detection aims to identify objects of interest from
unlabeled raw data, such as LiDAR points. Recent approaches usually adopt
pseudo 3D bounding boxes (3D bboxes) from clustering algorithm to initialize
the model training, and then iteratively updating both pseudo labels and the
trained model. However, pseudo bboxes inevitably contain noises, and such
inaccurate annotation accumulates to the final model, compromising the
performance. Therefore, in an attempt to mitigate the negative impact of pseudo
bboxes, we introduce a new uncertainty-aware framework. In particular, Our
method consists of two primary components: uncertainty estimation and
uncertainty regularization. (1) In the uncertainty estimation phase, we
incorporate an extra auxiliary detection branch alongside the primary detector.
The prediction disparity between the primary and auxiliary detectors is
leveraged to estimate uncertainty at the box coordinate level, including
position, shape, orientation. (2) Based on the assessed uncertainty, we
regularize the model training via adaptively adjusting every 3D bboxes
coordinates. For pseudo bbox coordinates with high uncertainty, we assign a
relatively low loss weight. Experiment verifies that the proposed method is
robust against the noisy pseudo bboxes, yielding substantial improvements on
nuScenes and Lyft compared to existing techniques, with increases of 6.9% in
AP$_{BEV}$ and 2.5% in AP$_{3D}$ on nuScenes, and 2.2% in AP$_{BEV}$ and 1.0%
in AP$_{3D}$ on Lyft.
