mlsanityData to 7 Oct 2026

Synthetic Depth-of-Field with a Single-Camera Mobile Phone

arXiv 1806.04171 ↗First posted 11 Jun 2018cs.CV cs.GRcited by 70

Not on the map. Why?

Abstract (arXiv)

Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background. However, standard cell phone cameras cannot produce such images optically, as their short focal lengths and small apertures capture nearly all-in-focus images. We present a system to computationally synthesize shallow depth-of-field images with a single mobile camera and a single button press. If the image is of a person, we use a person segmentation network to separate the person and their accessories from the background. If available, we also use dense dual-pixel auto-focus hardware, effectively a 2-sample light field with an approximately 1 millimeter baseline, to compute a dense depth map. These two signals are combined and used to render a defocused image. Our system can process a 5.4 megapixel image in 4 seconds on a mobile phone, is fully automatic, and is robust enough to be used by non-experts. The modular nature of our system allows it to degrade naturally in the absence of a dual-pixel sensor or a human subject.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 16; 2023: 9; 2024: 8; 2025: 12; 2026: 8.

70 papers.

Sort:

By area:

  1. 2021NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Imagescited by 336
  2. 20223D Common Corruptions and Data Augmentationcited by 120
  3. 2021Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Mergingcited by 104
  4. 2021Mobile Computational Photography: A Tourcited by 62
  5. 2022BokehMe: When Neural Rendering Meets Classical Renderingcited by 48
  6. 2024Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesiscited by 27
  7. 2022MyStyle: A Personalized Generative Priorcited by 26
  8. 2022DoF-NeRF: Depth-of-Field Meets Neural Radiance Fieldscited by 26
  9. 2021Defocus Map Estimation and Deblurring from a Single Dual-Pixel Imagecited by 25
  10. 2021Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervisioncited by 23
  11. 2021Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learningcited by 20
  12. 2021Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Imagecited by 18
38 more
  1. 2022Face Deblurring using Dual Camera Fusion on Mobile Phonescited by 18
  2. 2022MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effectscited by 17
  3. 2022AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fieldscited by 12
  4. 2023DC2: Dual-Camera Defocus Control by Learning to Refocuscited by 12
  5. 2021Facial Depth and Normal Estimation using Single Dual-Pixel Cameracited by 11
  6. 2021Neural Camera Simulatorscited by 10
  7. 2025Bokeh Diffusion: Defocus Blur Control in Text-to-Image Diffusion Modelscited by 10
  8. 2025Bokehlicious: Photorealistic Bokeh Rendering with Controllable Aperturescited by 10
  9. 2022NeRFocus: Neural Radiance Field for 3D Synthetic Defocuscited by 8
  10. 2022Learning Depth from Focus in the Wildcited by 8
  11. 2022Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photographycited by 8
  12. 2023Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priorscited by 8
  13. 2023My3DGen: A Scalable Personalized 3D Generative Modelcited by 8
  14. 2021Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Visioncited by 7
  15. 2023Learning Lens Blur Fieldscited by 6
  16. 2024Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imagingcited by 6
  17. 2024Dynamic Neural Radiance Field From Defocused Monocular Videocited by 6
  18. 2025DoF-Gaussian: Controllable Depth-of-Field for 3D Gaussian Splattingcited by 6
  19. 2022Degradation-agnostic Correspondence from Resolution-asymmetric Stereocited by 5
  20. 2022FloatingFusion: Depth from ToF and Image-stabilized Stereo Camerascited by 5
  21. 2025DiffCamera: Arbitrary Refocusing on Imagescited by 5
  22. 2021Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networkscited by 4
  23. 2021Correcting Face Distortion in Wide-Angle Videoscited by 4
  24. 2021Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Networkcited by 4
  25. 2024Scale-Invariant Monocular Depth Estimation via SSI Depthcited by 4
  26. 2021Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernelscited by 3
  27. 2022Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic Edge-Aware Bokehcited by 3
  28. 2022Adaptive Mask-based Pyramid Network for Realistic Bokeh Renderingcited by 3
  29. 2023AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environmentcited by 3
  30. 2023Continuous Cost Aggregation for Dual-Pixel Disparity Extractioncited by 3
  31. 2023Towards Real-World Focus Stacking with Deep Learningcited by 3
  32. 2025BokehFlow: Depth-Free Controllable Bokeh Rendering via Flow Matchingcited by 3
  33. 2021Procedural Kernel Networkscited by 2
  34. 2024Depth from Coupled Optical Differentiationcited by 2
  35. 2026The First Controllable Bokeh Rendering Challenge at NTIRE 2026cited by 2
  36. 2021BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurringcited by 1
  37. 2022Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wildcited by 1
  38. 2024Disparity Estimation Using a Quad-Pixel Sensorcited by 1

50 most cited of 70 All 70 (JSON)