Multiview Equivariance Improves 3D Correspondence Understanding with
  Minimal Feature Finetuning
Authors: name: Yang You, name: Yixin Li, name: Congyue Deng, name: Yue Wang, name: 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 correspondence
understanding of existing vision models. Remarkably, even finetuning on a
single object for just one iteration results in substantial performance gains.
All code and resources will be made publicly available to support further
advancements in 3D-aware vision models. Our code is available at
https://github.com/qq456cvb/3DCorrEnhance.