FMDP: Leveraging a Foundation Model for Dual-Pixel Disparity Estimation

Doehyung Lee, Zhuofeng Wu, Yusuke Monno, Masatoshi Okutomi

Abstract: In this paper, we propose a foundation-model-aided dual-pixel disparity estimation network, named FMDP, which leverages the physical cues from dual-pixel de-focus disparities and the powerful scene priors encoded by a depth-estimation foundation model. Previous dual-pixel disparity estimation methods often suffer from limited generalization ability due to the lack of a large-scale training dataset. In contrast, recent depth-estimation foundation models can successfully encode the features of diverse real scenes using a huge amount of data. Given this, our FMDP effectively integrates the features from a foundation model into a dual-pixel disparity estimation pipeline. Experimental results show that our FMDP consistently outperforms prior methods on both synthetic and real scenes, especially demonstrating improved robustness to noise and strong generalization to unseen real scenes.

External IDs:dblp:conf/mva/LeeWMO25