  Monocular 3D object detection is an essential perception task for autonomous
driving. However, the high reliance on large-scale labeled data make it costly
and time-consuming during model optimization. To reduce such over-reliance on
human annotations, we propose Mix-Teaching, an effective semi-supervised
learning framework applicable to employ both labeled and unlabeled images in
training stage. Mix-Teaching first generates pseudo-labels for unlabeled images
by self-training. The student model is then trained on the mixed images
possessing much more intensive and precise labeling by merging instance-level
image patches into empty backgrounds or labeled images. This is the first to
break the image-level limitation and put high-quality pseudo labels from multi
frames into one image for semi-supervised training. Besides, as a result of the
misalignment between confidence score and localization quality, it's hard to
discriminate high-quality pseudo-labels from noisy predictions using only
confidence-based criterion. To that end, we further introduce an
uncertainty-based filter to help select reliable pseudo boxes for the above
mixing operation. To the best of our knowledge, this is the first unified SSL
framework for monocular 3D object detection. Mix-Teaching consistently improves
MonoFlex and GUPNet by significant margins under various labeling ratios on
KITTI dataset. For example, our method achieves around +6.34% AP@0.7
improvement against the GUPNet baseline on validation set when using only 10%
labeled data. Besides, by leveraging full training set and the additional 48K
raw images of KITTI, it can further improve the MonoFlex by +4.65% improvement
on AP@0.7 for car detection, reaching 18.54% AP@0.7, which ranks the 1st place
among all monocular based methods on KITTI test leaderboard. The code and
pretrained models will be released at
https://github.com/yanglei18/Mix-Teaching.

Computer Vision and Pattern Recognition