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.

翻译：点云数据在机器人与自动驾驶应用中发挥着至关重要的作用。然而，标注点云数据既耗时又困难，而它们能学习到赋予下游任务（如分类与分割）能力的判别性三维表示。近年来，基于对比学习的框架在自监督学习三维表示方面展现出令人瞩目的成果。但现有对比学习方法无法精确编码并关联结构特征，也难以高效搜索高维增强空间。本文提出CLR-GAM——一种新颖的基于对比学习的框架，它采用引导增强（GA）实现高效的动态探索策略，并利用引导特征映射（GFM）实现增强点云间相似结构特征的关联。实验证明，所提方法在模拟与真实三维点云数据集上，针对三维点云分类、小样本学习及物体部件分割三种不同下游任务均取得了最优性能。