Disparity Estimation Using a Quad-Pixel Sensor


Zhuofeng Wu (Tokyo Institute of Technology, Tokyo Institute of Technology), Doehyung Lee (Tokyo Institute of Technology, Tokyo Institute of Technology), Zihua Liu (Tokyo Institute of Technology, Tokyo Institute of Technology), Kazunori Yoshizaki (Olympus Medical Systems Corporation), Yusuke Monno (Institute of Science Tokyo), Masatoshi Okutomi (Tokyo Institute of Technology)
The 35th British Machine Vision Conference
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Abstract

A quad-pixel (QP) sensor is increasingly integrated into commercial mobile cameras. The QP sensor has a unit of 2×2 four photodiodes under a single microlens, generating multi-directional phase shifting when out-focus blurs occur. Similar to a dual-pixel (DP) sensor, the phase shifting can be regarded as stereo disparity and utilized for depth estimation. Based on this, we propose a QP disparity estimation network (QPDNet), which exploits abundant QP information by fusing vertical and horizontal stereo-matching correlations for effective disparity estimation. We also present a synthetic pipeline to generate a training dataset from an existing RGB-Depth dataset. Experimental results demonstrate that our QPDNet outperforms state-of-the-art stereo and DP methods. Our code and synthetic dataset are available at https://github.com/Zhuofeng-Wu/QPDNet.

Citation

@inproceedings{Wu_2024_BMVC,
author    = {Zhuofeng Wu and Doehyung Lee and Zihua Liu and Kazunori Yoshizaki and Yusuke Monno and Masatoshi Okutomi},
title     = {Disparity Estimation Using a Quad-Pixel Sensor},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0480.pdf}
}


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