Pre-Training by Completing Point Clouds

28 Sep 2020  ·  Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt Kusner ·

There has recently been a flurry of exciting advances in deep learning models on point clouds. However, these advances have been hampered by the difficulty of creating labelled point cloud datasets: sparse point clouds often have unclear label identities for certain points, while dense point clouds are time-consuming to annotate. Inspired by mask-based pre-training in the natural language processing community, we propose a pre-training mechanism based point clouds completion. It works by masking occluded points that result from observations at different camera views. It then optimizes a completion model that learns how to reconstruct the occluded points, given the partial point cloud. In this way, our method learns a pre-trained representation that can identify the visual constraints inherently embedded in real-world point clouds. We call our method Occlusion Completion (OcCo). We demonstrate that OcCo learns representations that improve the semantic understandings as well as generalization on downstream tasks over prior methods, transfer to different datasets, reduce training time and improve label efficiency.

PDF Abstract
No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here