  Deep learning has attained remarkable success in many 3D visual recognition
tasks, including shape classification, object detection, and semantic
segmentation. However, many of these results rely on manually collecting
densely annotated real-world 3D data, which is highly time-consuming and
expensive to obtain, limiting the scalability of 3D recognition tasks. Thus, we
study unsupervised 3D recognition and propose a Self-supervised-Self-Labeled 3D
Recognition (SL3D) framework. SL3D simultaneously solves two coupled
objectives, i.e., clustering and learning feature representation to generate
pseudo-labeled data for unsupervised 3D recognition. SL3D is a generic
framework and can be applied to solve different 3D recognition tasks, including
classification, object detection, and semantic segmentation. Extensive
experiments demonstrate its effectiveness. Code is available at
https://github.com/fcendra/sl3d.
