基于全局与局部特征对比的点云自监督学习

Self-Supervised Learning on Point Clouds by Contrasting Global and Local Features

传统的有监督学习依赖大量的标签数据,而收集标签数据通常是昂贵的.因此,提出一种通过对比点云的全局和局部特征的自监督学习算法,包括数据构造和对比学习2个阶段.在数据构造阶段,通过不同的局部视角和局部子结构生成全局物体的局部区域;在对比学习阶段,将全局物体和局部区域分别依次输入编码器、投影层和预测器得到全局和局部特征,使用基于对比学习的目标函数增强全局和局部特征相似.通过在2个公开数据集ModelNet40和ShapeNet上与Info3D等自监督学习算法对比,实验结果表明,所提算法在无监督点云分类和小样本学习任务上的分类准确率得到显著提升,并且在训练数据匮乏时比现有算法具有更强的鲁棒性.

Traditional supervised learning methods rely on large amounts of labeled data,while collecting labeled data with high quality is expensive.To this end,a self-supervised learning method based on contrasting global and local features of point clouds is proposed,which includes two stages:data construction and contrastive learning.In the data construction phase,local regions are generated by partial scanning from different views and cropping local substructures.In the contrastive learning phase,the global object and the local region are sent to the encoder,the projection layer and the predictor in sequence to get the global and local features respectively,and the objective function based on the contrastive learning is used to enhance the similarity between global and local features.The experimental results on two public datasets including Model Net40 and ShapeNet show that compared with other methods such as Info3D,the proposed method can significantly improve the classification accuracy in both unsupervised point cloud classification and few-shot learning tasks,and it is more robust than existing methods when training data is scarce.