mlsanityData to 7 Oct 2026

Disparity Estimation Using a Quad-Pixel Sensor

arXiv 2409.00665 ↗First posted 1 Sep 2024cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 1 Sep 2024.

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26 of 36 references.

References · 26

  1. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  2. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
  3. 2020Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understandingcited by 593
  4. 2018Pyramid Stereo Matching Networkcited by 486
  5. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 359
  6. 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 348
  7. 2019FFA-Net: Feature Fusion Attention Network for Single Image Dehazingcited by 307
  8. 2019Structured3D: A Large Photo-realistic Dataset for Structured 3D Modelingcited by 285
  9. 2022Unifying Flow, Stereo and Depth Estimationcited by 270
  10. 2019GA-Net: Guided Aggregation Net for End-to-end Stereo Matchingcited by 242
  11. 2019Group-wise Correlation Stereo Networkcited by 235
  12. 2020Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformerscited by 190
14 more
  1. 2023Iterative Geometry Encoding Volume for Stereo Matchingcited by 188
  2. 2021CFNet: Cascade and Fused Cost Volume for Robust Stereo Matchingcited by 153
  3. 2020Defocus Deblurring Using Dual-Pixel Datacited by 138
  4. 2022Attention Concatenation Volume for Accurate and Efficient Stereo Matchingcited by 130
  5. 2018Synthetic Depth-of-Field with a Single-Camera Mobile Phonecited by 70
  6. 2019Learning Single Camera Depth Estimation using Dual-Pixelscited by 49
  7. 2020Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Datacited by 42
  8. 2020Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restorationcited by 34
  9. 2021Defocus Map Estimation and Deblurring from a Single Dual-Pixel Imagecited by 25
  10. 2020Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixelscited by 23
  11. 2023Global Occlusion-Aware Transformer for Robust Stereo Matchingcited by 19
  12. 2021Facial Depth and Normal Estimation using Single Dual-Pixel Cameracited by 11
  13. 2022Dual-Pixel Raindrop Removalcited by 6
  14. 2024Digging Into Normal Incorporated Stereo Matchingcited by 5

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Citing papers per year: 2025: 1.

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  1. 2025Simulating Dual-Pixel Images From Ray Tracing For Depth Estimationcited by 1