DepthCNE: Contrastive Neighbor Embedding in Self-Supervised Learning for Point Clouds

Mingyu Liu, Zhijie Yang, Ekim Yurtsever, Yuning Cui, Alois Knoll

Abstract: Contrastive learning, as one of the self-supervised learning paradigms, has demonstrated substantial effectiveness in boosting various downstream tasks. However, its inherent lack of explicit control over the embedding space structure often results in suboptimal performance and limited feature visualization. To address these challenges, we propose DepthCNE, an innovative self-supervised learning framework for point clouds that integrates contrastive learning with dimensionality reduction. Specifically, DepthCNE utilizes a 3D point-based encoder and a projection head, consistent with conventional contrastive learning designs. In addition, we introduce a dimensionality reduction head, which projects high-dimensional features into a two-dimensional space to improve the model’s representation capability. Extensive experiments demonstrate the competitive performance of DepthCNE on both synthetic and real-world datasets across various downstream tasks. Moreover, our method exhibits promising performance in unsupervised visualization. The codes are available at https://github.com/MingyuLiu1/DepthCNE.git.

External IDs:dblp:conf/ijcnn/LiuYYCK25