CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping

Srikanth Malla, Yi-Ting Chen

Keywords: contrastive learning, point cloud representation learning, few shot learning, self supervised learning

TL;DR: efficient augmentation selection strategy, and effective feature association in contrastive learning

Abstract: Point cloud data plays an essential role in robotics and self-driving applications. Yet, it is time-consuming and nontrivial to annotate point cloud data while they enable learning discriminative 3D representations that empower downstream tasks, such as classification and segmentation. Recently, contrastive learning based frameworks show promising results for learning 3D representations in a self-supervised manner. However, existing contrastive learning methods cannot encode and associate structural features precisely and search the higher dimensional augmentation space efficiently. In this paper, we present CLR-GAM, a novel contrastive learning based framework with Guided Augmentation (GA) for efficient dynamic exploration strategy and Guided Feature Mapping (GFM) for similar structural feature association between augmented point clouds. We empirically demonstrate that the proposed approach achieves state-of-the-art performance on both simulated and real-world 3D point cloud datasets for three different downstream tasks, i.e., 3D point cloud classification, few-shot learning, and object part segmentation. The code and pretrained models are made available in the supplementary material.

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