Improving 2D Feature Representations by 3D-Aware Fine-Tuning
Authors: name: Yuanwen Yue; name: Anurag Das; name: Francis Engelmann; name: Siyu Tang; name: Jan Eric Lenssen
Published: 2024-07-29 17:59:21

Abstract: Current visual foundation models are trained purely on unstructured 2D data,
limiting their understanding of 3D structure of objects and scenes. In this
work, we show that fine-tuning on 3D-aware data improves the quality of
emerging semantic features. We design a method to lift semantic 2D features
into an efficient 3D Gaussian representation, which allows us to re-render them
for arbitrary views. Using the rendered 3D-aware features, we design a
fine-tuning strategy to transfer such 3D awareness into a 2D foundation model.
We demonstrate that models fine-tuned in that way produce features that readily
improve downstream task performance in semantic segmentation and depth
estimation through simple linear probing. Notably, though fined-tuned on a
single indoor dataset, the improvement is transferable to a variety of indoor
datasets and out-of-domain datasets. We hope our study encourages the community
to consider injecting 3D awareness when training 2D foundation models. Project
page: https://ywyue.github.io/FiT3D.