Reweighted Contrastive Learning for Robust Point Cloud Representations

Contrastive self-supervised learning has achieved promising performance in 3D point cloud representation learning, typically using the standard NT-Xent loss. However, NT-Xent assigns the strongest repulsive gradients to high-similarity negatives, including false negatives that share similar geometric semantics, which can disrupt intra-class compactness and increase sensitivity to sampling noise. To address this issue, we propose Reweighted Contrastive Loss for Robust Representations (ReCoR²), derived from a maximum likelihood estimation perspective. ReCoR² treats anchor–candidate similarity as evidence of shared latent geometric semantics, applies weaker repulsion to higher-similarity candidates, and retains strong repulsion for low-similarity ones with larger geometric differences. Since similarity estimates can be unreliable early in training, we adopt a two-stage optimization strategy: NT-Xent is first used to establish a semantically structured representation space, followed by optimization with ReCoR². Theoretically, we establish an upper bound on the gradient norm of ReCoR² with respect to sampling noise, which is strictly lower than the corresponding lower bound for standard NT-Xent, demonstrating improved robustness to sampling perturbations. Integrated into a 3D–2D cross-modal pretraining framework, ReCoR² consistently improves performance over representative contrastive baselines on downstream classification and part segmentation tasks.