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

TL;DR

CLR-GAM introduces a contrastive learning framework with guided augmentation and feature mapping to improve 3D point cloud representations, achieving state-of-the-art results in classification, few-shot learning, and segmentation.

Contribution

It proposes novel guided augmentation and feature mapping techniques to enhance contrastive learning for 3D point clouds, addressing structural feature encoding and augmentation space exploration.

Findings

Achieves state-of-the-art performance on multiple 3D tasks

Effective in both simulated and real-world datasets

Improves structural feature association in point cloud representations

Abstract

Point cloud data plays an essential role in robotics and self-driving applications. Yet, annotating point cloud data is time-consuming and nontrivial while they enable learning discriminative 3D representations that empower downstream tasks, such as classification and segmentation. Recently, contrastive learning-based frameworks have shown promising results for learning 3D representations in a self-supervised manner. However, existing contrastive learning methods cannot precisely encode and associate structural features 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.