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

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.