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AIM 2020 Challenge on Rendering Realistic Bokeh

arXiv 2011.04988 ↗First posted 10 Nov 2020eess.IV cs.CVcited by 26

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Abstract (arXiv)

This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world bokeh simulation problem, where the goal was to learn a realistic shallow focus technique using a large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The participants had to render bokeh effect based on only one single frame without any additional data from other cameras or sensors. The target metric used in this challenge combined the runtime and the perceptual quality of the solutions measured in the user study. To ensure the efficiency of the submitted models, we measured their runtime on standard desktop CPUs as well as were running the models on smartphone GPUs. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Nov 2020.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 4; 2024: 1; 2025: 4; 2026: 1.

26 papers.

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  1. 2023Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Modelscited by 86
  2. 2022BokehMe: When Neural Rendering Meets Classical Renderingcited by 48
  3. 2021Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 34
  4. 2021Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Reportcited by 34
  5. 2022DoF-NeRF: Depth-of-Field Meets Neural Radiance Fieldscited by 26
  6. 2021Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Reportcited by 19
  7. 2021Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 19
  8. 2022MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effectscited by 17
  9. 2022Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Reportcited by 16
  10. 2021Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 15
  11. 2022Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Reportcited by 13
  12. 2021Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Reportcited by 10
14 more
  1. 2025Bokehlicious: Photorealistic Bokeh Rendering with Controllable Aperturescited by 10
  2. 2023Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priorscited by 8
  3. 2025BokehDiff: Neural Lens Blur with One-Step Diffusioncited by 8
  4. 2024Dynamic Neural Radiance Field From Defocused Monocular Videocited by 6
  5. 2025DoF-Gaussian: Controllable Depth-of-Field for 3D Gaussian Splattingcited by 6
  6. 2022Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Reportcited by 4
  7. 2022Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic Edge-Aware Bokehcited by 3
  8. 2022Adaptive Mask-based Pyramid Network for Realistic Bokeh Renderingcited by 3
  9. 2022Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Reportcited by 3
  10. 2023BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens Metadata Embeddingcited by 3
  11. 2022Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Reportcited by 2
  12. 2026The First Controllable Bokeh Rendering Challenge at NTIRE 2026cited by 2
  13. 2023GBSD: Generative Bokeh with Stage Diffusioncited by 0
  14. 2025MODEST: Multi-Optics Depth-of-Field Stereo Datasetcited by 0