Computing accurate depth from multiple views is a fundamental and longstanding challenge in computer vision. However, most existing approaches do not generalize well across different domains and scene types (e.g. indoor vs. outdoor). Training a general-purpose multi-view stereo model is challenging and raises several questions, e.g. how to best make use of transformer-based architectures, how to incorporate additional metadata when there is a variable number of input views, and how to estimate the range of valid depths which can vary considerably across different scenes and is typically not known a priori? To address these issues, we introduce MVSA, a novel and versatile Multi-View Stereo architecture that aims to work Anywhere by generalizing across diverse domains and depth ranges. MVSA combines monocular and multi-view cues with an adaptive cost volume to deal with scale-related issues. We demonstrate state-of-the-art zero-shot depth estimation on the Robust Multi-View Depth Benchmark, surpassing existing multi-view stereo and monocular baselines.
We construct a general-purpose multi-view depth estimation model. We start with a cost-volume based architecture, which matches deep features between views at different hypothesized depths. Key for performance are our Cost Volume Patchifier and Mono/Multi Cue Combiner. These also fuse single-view information coming from the Reference Image Encoder and source views.
Our cost volume patchifier enables high-quality infor- mation to be extracted from a |D| × H/4 × W/4 cost volume, ready for input to the Mono/Multi Cue Combiner ViT. (a) Shows the naive approach to patchification. (b) Our approach makes better use of the reference image features.
If you find this work useful for your research, please cite:
@inproceedings{izquierdo2025mvsanywhere,
title={{MVSAnywhere}: Zero Shot Multi-View Stereo},
author={Izquierdo, Sergio and Sayed, Mohamed and Firman, Michael and Garcia-Hernando, Guillermo and Turmukhambetov, Daniyar and Civera, Javier and Mac Aodha, Oisin and Brostow, Gabriel J. and Watson, Jamie},
booktitle={CVPR},
year={2025}
}
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