Disparity Estimation Using a Quad-Pixel Sensor
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929482163552256 |
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| author | Wu, Zhuofeng Lee, Doehyung Liu, Zihua Yoshizaki, Kazunori Monno, Yusuke Okutomi, Masatoshi |
| author_facet | Wu, Zhuofeng Lee, Doehyung Liu, Zihua Yoshizaki, Kazunori Monno, Yusuke Okutomi, Masatoshi |
| contents | A quad-pixel (QP) sensor is increasingly integrated into commercial mobile cameras. The QP sensor has a unit of 2$\times$2 four photodiodes under a single microlens, generating multi-directional phase shifting when out-focus blurs occur. Similar to a dual-pixel (DP) sensor, the phase shifting can be regarded as stereo disparity and utilized for depth estimation. Based on this, we propose a QP disparity estimation network (QPDNet), which exploits abundant QP information by fusing vertical and horizontal stereo-matching correlations for effective disparity estimation. We also present a synthetic pipeline to generate a training dataset from an existing RGB-Depth dataset. Experimental results demonstrate that our QPDNet outperforms state-of-the-art stereo and DP methods. Our code and synthetic dataset are available at https://github.com/Zhuofeng-Wu/QPDNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Disparity Estimation Using a Quad-Pixel Sensor Wu, Zhuofeng Lee, Doehyung Liu, Zihua Yoshizaki, Kazunori Monno, Yusuke Okutomi, Masatoshi Computer Vision and Pattern Recognition A quad-pixel (QP) sensor is increasingly integrated into commercial mobile cameras. The QP sensor has a unit of 2$\times$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. |
| title | Disparity Estimation Using a Quad-Pixel Sensor |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.00665 |