Graph Spectral Perturbation for 3D Point Cloud Contrastive Learning

3D point cloud contrastive learning has attracted increasing attention due to its efficient learning ability. By distinguishing the similarity relationship between positive and negative samples in the feature space, it can learn effective point cloud feature representations without manual annotation. However, most point cloud contrastive learning methods construct contrastive samples by perturbing point clouds in data space or introducing multi-modality/format data, which may be difficult to control the intensity of the perturbation or introduce interference from different modalities/formats. To this end, in this paper, we propose a novel graph spectral perturbation based contrastive learning framework (GSPCon) for efficient and robust self-supervised 3D point cloud representation learning. It aims to perform perturbations in the graph spectral domain to construct contrastive samples of the point cloud. Specifically, we first naturally represent the point cloud as a k-nearest neighbors (KNN) graph, and adaptively transform the coordinates of the points into the graph spectral domain based on the graph Fourier transform (GFT). Then we implement data augmentation in the graph spectral domain by perturbing the spectral representations. Finally, the contrastive samples are generated by employing the inverse graph Fourier transform (IGFT) to transform the augmented spectral representations back to the point clouds. Experimental results show that our method achieves the state-of-the-art performance on various downstream tasks. Source code is available at https://github.com/yh-han/GSPCon.git.