Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging
Bhargav Ghanekar, Salman Siddique Khan, Pranav Sharma, Shreyas Singh, Vivek Boominathan, Kaushik Mitra, Ashok Veeraraghavan
Abstract
Passive, compact, single-shot 3D sensing is useful in many application areas such as microscopy, medical imaging, surgical navigation, and autonomous driving where form factor, time, and power constraints can exist. Obtaining RGB-D scene information over a short imaging distance, in an ultra-compact form factor, and in a passive, snapshot manner is challenging. Dual-pixel (DP) sensors are a potential solution to achieve the same. DP sensors collect light rays from two different halves of the lens in two interleaved pixel arrays, thus capturing two slightly different views of the scene, like a stereo camera system. However, imaging with a DP sensor implies that the defocus blur size is directly proportional to the disparity seen between the views. This creates a trade-off between disparity estimation vs. deblurring accuracy. To improve this trade-off effect, we propose CADS (Coded Aperture Dual-Pixel Sensing), in which we use a coded aperture in the imaging lens along with a DP sensor. In our approach, we jointly learn an optimal coded pattern and the reconstruction algorithm in an end-to-end optimization setting. Our resulting CADS imaging system demonstrates improvement of >1.5 dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings. Furthermore, we build the proposed CADS prototypes for DSLR photography settings and in an endoscope and a dermoscope form factor. Our novel coded dual-pixel sensing approach demonstrates accurate RGB-D reconstruction results in simulations and real-world experiments in a passive, snapshot, and compact manner.
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Install the CLIlune papers fulltext ebe6c506-9158-4170-a2c9-a7c53e8c680eCited by top-tier papers4
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Builds on6
- Learning Single Camera Depth Estimation Using Dual-PixelsRahul Garg, Neal Wadhwa, Sameer Ansari, Jonathan T. BarronICCV 2019 · 123 citations
- Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel DataAbdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S. Brown et al.ICCV 2021 · 72 citations
- Defocus Map Estimation and Deblurring from a Single Dual-Pixel ImageShumian Xin, Neal Wadhwa, Tianfan Xue, Jonathan T. Barron et al.ICCV 2021 · 47 citations
- Dual Pixel Exploration: Simultaneous Depth Estimation and Image RestorationLiyuan Pan, Shah Chowdhury, Richard Hartley, Miaomiao Liu et al.CVPR 2021
- Spatio-Focal Bidirectional Disparity Estimation from a Dual-Pixel ImageDonggun Kim, Hyeonjoong Jang, Inchul Kim, Min H. KimCVPR 2023
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