  Image-based 3D detection is an indispensable component of the perception
system for autonomous driving. However, it still suffers from the unsatisfying
performance, one of the main reasons for which is the limited training data.
Unfortunately, annotating the objects in the 3D space is extremely
time/resource-consuming, which makes it hard to extend the training set
arbitrarily. In this work, we focus on the semi-supervised manner and explore
the feasibility of a cheaper alternative, i.e. pseudo-labeling, to leverage the
unlabeled data. For this purpose, we conduct extensive experiments to
investigate whether the pseudo-labels can provide effective supervision for the
baseline models under varying settings. The experimental results not only
demonstrate the effectiveness of the pseudo-labeling mechanism for image-based
3D detection (e.g. under monocular setting, we achieve 20.23 AP for moderate
level on the KITTI-3D testing set without bells and whistles, improving the
baseline model by 6.03 AP), but also show several interesting and noteworthy
findings (e.g. the models trained with pseudo-labels perform better than that
trained with ground-truth annotations based on the same training data). We hope
this work can provide insights for the image-based 3D detection community under
a semi-supervised setting. The codes, pseudo-labels, and pre-trained models
will be publicly available.

Computer Vision and Pattern Recognition