Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels
This work addresses the need for more efficient and label-free adaptation of 3D vision models in real-world applications, though it is incremental as it builds on existing cross-modal alignment methods.
The paper tackles the problem of improving zero-shot 3D vision models for classification without labels by proposing Cross-MoST, a cross-modal self-training framework that leverages unlabeled 3D data and 2D views, resulting in enhanced classification performance through joint pseudo-labeling and feature alignment.
Large-scale vision 2D vision language models, such as CLIP can be aligned with a 3D encoder to learn generalizable (open-vocabulary) 3D vision models. However, current methods require supervised pre-training for such alignment, and the performance of such 3D zero-shot models remains sub-optimal for real-world adaptation. In this work, we propose an optimization framework: Cross-MoST: Cross-Modal Self-Training, to improve the label-free classification performance of a zero-shot 3D vision model by simply leveraging unlabeled 3D data and their accompanying 2D views. We propose a student-teacher framework to simultaneously process 2D views and 3D point clouds and generate joint pseudo labels to train a classifier and guide cross-model feature alignment. Thereby we demonstrate that 2D vision language models such as CLIP can be used to complement 3D representation learning to improve classification performance without the need for expensive class annotations. Using synthetic and real-world 3D datasets, we further demonstrate that Cross-MoST enables efficient cross-modal knowledge exchange resulting in both image and point cloud modalities learning from each other's rich representations.