Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning
Yang You Yixin Li Congyue Deng Yue Wang Leonidas Guibas
Vision foundation models, particularly the ViT family, have revolutionized image understanding by providing rich semantic features. However, despite their success in 2D comprehension, their abilities on grasping 3D spatial relationships are still unclear.
In this work, we evaluate and enhance the 3D awareness of ViT-based models. We begin by systematically assessing their ability to learn 3D equivariant features, specifically examining the consistency of semantic embeddings across different viewpoints. Our findings indicate that improved 3D equivariance leads to better performance on various downstream tasks, including pose estimation, tracking, and semantic transfer. Building on this insight, we propose a simple yet effective finetuning strategy based on 3D correspondences, which significantly enhances the 3D understanding of existing vision models. Remarkably, even finetuning on a single object for just one iteration results in substantial performance gains. Code is available on https://github.com/qq456cvb/3DCorrEnhance.
Vision Foundation Models; 3D Representation Learning; Fine-tuning; 3D Equivariance
This work evaluates and improves the 3D awareness of ViT-based models by enhancing their 3D equivariance through a simple finetuning strategy using 3D correspondences.