This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely on the fully-supervised pretrain with large annotated datasets, which causes the learned feature extraction bias to those pretrained classes. However, as the purpose of few-shot learning is to handle unknown/unseen classes, such class-specific feature extraction in pretrain is not ideal to generalize into new classes for few-shot learning. Moreover, point cloud datasets hardly have a large number of classes due to the annotation difficulty. To address these issues, we propose a contrastive self-supervision framework for few-shot learning pretrain, which aims to eliminate the feature extraction bias through class-agnostic contrastive supervision. Specifically, we implement a novel contrastive learning approach with a learnable augmentor for a 3D point cloud to achieve point-wise differentiation, so that to enhance the pretrain with managed overfitting through the self-supervision. Furthermore, we develop a multi-resolution attention module using both the nearest and farthest points to extract the local and global point information more effectively, and a center-concentrated multi-prototype is adopted to mitigate the intra-class sparsity. Comprehensive experiments are conducted to evaluate the proposed approach, which shows our approach achieves state-of-the-art performance. Moreover, a case study on practical CAM/CAD segmentation is presented to demonstrate the effectiveness of our approach for real-world applications.

翻译：本文提出了一种针对实际应用的高效小样本点云语义分割方法。现有基于点云的小样本分割方法严重依赖大规模标注数据集的全监督预训练，这导致学到的特征提取偏向于这些预训练类别。然而，由于小样本学习的目的是处理未知/未见类别，这种预训练中的类别特定特征提取难以泛化到小样本学习中的新类别。此外，因标注困难，点云数据集通常不具备大量类别。为解决这些问题，我们提出了一种用于小样本学习预训练的对比自监督框架，旨在通过类别无关的对比监督消除特征提取偏差。具体而言，我们实现了一种新颖的对比学习方法，该方法采用可学习增强器对三维点云进行逐点区分，从而通过自监督增强预训练并控制过拟合。此外，我们开发了一个多分辨率注意力模块，利用最近点和最远点更有效地提取局部与全局点信息，并采用中心集中式多原型来缓解类内稀疏性问题。通过全面实验对所提方法进行了评估，结果表明我们的方法达到了最先进的性能。同时，通过一个实际的CAM/CAD分割案例研究，验证了该方法在真实应用中的有效性。