Improving 2D Feature Representations by 3D-Aware Fine-Tuning

• URL: http://arxiv.org/abs/2407.20229v1
• Date: Mon, 29 Jul 2024 17:59:21 GMT
• Title: Improving 2D Feature Representations by 3D-Aware Fine-Tuning
• Authors: Yuanwen Yue, Anurag Das, Francis Engelmann, Siyu Tang, Jan Eric Lenssen,
• Abstract summary: Current visual foundation models are trained purely on unstructured 2D data. We show that fine-tuning on 3D-aware data improves the quality of emerging semantic features.
• Score: 17.01280751430423
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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