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Data Efficient 3D Learner via Knowledge Transferred from 2D Model
[article]
2022
arXiv
pre-print
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
arXiv:2203.08479v3
fatcat:43mdeh7q4zbq5dyccjf46wyf4u