mlsanityData to 7 Oct 2026

Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging

arXiv 2402.18102 ↗First posted 28 Feb 2024eess.IV cs.CVcited by 6

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Abstract (arXiv)

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 >1.5dB 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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 42 references that are in the map. First posted on arXiv 28 Feb 2024.

Builds on

16 of 42 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2016Pyramid Scene Parsing Networkcited by 2,472
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  6. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  7. 2017Road Extraction by Deep Residual U-Netcited by 333
  8. 2020Defocus Deblurring Using Dual-Pixel Datacited by 138
  9. 2018Synthetic Depth-of-Field with a Single-Camera Mobile Phonecited by 70
  10. 2019Learning Single Camera Depth Estimation using Dual-Pixelscited by 49
  11. 2020Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Datacited by 42
  12. 2020Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restorationcited by 34
4 more
  1. 2021Defocus Map Estimation and Deblurring from a Single Dual-Pixel Imagecited by 25
  2. 2020Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixelscited by 23
  3. 2021Facial Depth and Normal Estimation using Single Dual-Pixel Cameracited by 11
  4. 2017Data Driven Coded Aperture Design for Depth Recoverycited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.