  Collecting and labeling the registered 3D point cloud is costly. As a result,
3D resources for training are typically limited in quantity compared to the 2D
images counterpart. In this work, we deal with the data scarcity challenge of
3D tasks by transferring knowledge from strong 2D models via RGB-D images.
Specifically, we utilize a strong and well-trained semantic segmentation model
for 2D images to augment RGB-D images with pseudo-label. The augmented dataset
can then be used to pre-train 3D models. Finally, by simply fine-tuning on a
few labeled 3D instances, our method already outperforms existing
state-of-the-art that is tailored for 3D label efficiency. We also show that
the results of mean-teacher and entropy minimization can be improved by our
pre-training, suggesting that the transferred knowledge is helpful in
semi-supervised setting. We verify the effectiveness of our approach on two
popular 3D models and three different tasks. On ScanNet official evaluation, we
establish new state-of-the-art semantic segmentation results on the
data-efficient track.
