  Semi-supervised 3D object detection from point cloud aims to train a detector
with a small number of labeled data and a large number of unlabeled data. The
core of existing methods lies in how to select high-quality pseudo-labels using
the designed quality evaluation criterion. However, these methods treat each
pseudo bounding box as a whole and assign equal importance to each side during
training, which is detrimental to model performance due to many sides having
poor localization quality. Besides, existing methods filter out a large number
of low-quality pseudo-labels, which also contain some correct regression values
that can help with model training. To address the above issues, we propose a
side-aware framework for semi-supervised 3D object detection consisting of
three key designs: a 3D bounding box parameterization method, an uncertainty
estimation module, and a pseudo-label selection strategy. These modules work
together to explicitly estimate the localization quality of each side and
assign different levels of importance during the training phase. Extensive
experiment results demonstrate that the proposed method can consistently
outperform baseline models under different scenes and evaluation criteria.
Moreover, our method achieves state-of-the-art performance on three datasets
with different labeled ratios.
