Self-Supervised Pretraining of 3D Features on any Point-Cloud

Understand

Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like 2D object recognition, video classification etc.

• However, pretraining is not widely used for 3D recognition tasks where state-of-the-art methods train models from scratch.
• A primary reason is the lack of large annotated datasets because 3D data is both difficult to acquire and time consuming to label.
• We present a simple self-supervised pertaining method that can work with any 3D data - single or multiview, indoor or outdoor, acquired by varied sensors, without 3D registration.