# Learning Depth from Focus in the Wild

arXiv: https://arxiv.org/abs/2207.09658 · HTML: https://mlsanity.com/paper/2207.09658/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [Single-View View Synthesis with Multiplane Images](https://mlsanity.com/paper/2004.11364/) (2020)
- [CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching](https://mlsanity.com/paper/2104.04314/) (2021)
- [Defocus Deblurring Using Dual-Pixel Data](https://mlsanity.com/paper/2005.00305/) (2020)
- [DPSNet: End-to-end Deep Plane Sweep Stereo](https://mlsanity.com/paper/1905.00538/) (2019)
- [Synthetic Depth-of-Field with a Single-Camera Mobile Phone](https://mlsanity.com/paper/1806.04171/) (2018)
- [DDFlow: Learning Optical Flow with Unlabeled Data Distillation](https://mlsanity.com/paper/1902.09145/) (2019)
- [Learning Single Camera Depth Estimation using Dual-Pixels](https://mlsanity.com/paper/1904.05822/) (2019)
- [Single Image Depth Estimation Trained via Depth from Defocus Cues](https://mlsanity.com/paper/2001.05036/) (2020)
- [Focus on defocus: bridging the synthetic to real domain gap for depth estimation](https://mlsanity.com/paper/2005.09623/) (2020)
- [Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration](https://mlsanity.com/paper/2012.00301/) (2020)
- [Deep Depth From Focus](https://mlsanity.com/paper/1704.01085/) (2017)
- [Learning to Autofocus](https://mlsanity.com/paper/2004.12260/) (2020)
- [Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image](https://mlsanity.com/paper/2110.05655/) (2021)
- [Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels](https://mlsanity.com/paper/2003.14299/) (2020)
- [Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision](https://mlsanity.com/paper/2108.10843/) (2021)
- [Aperture Supervision for Monocular Depth Estimation](https://mlsanity.com/paper/1711.07933/) (2017)
- [Variational Depth from Focus Reconstruction](https://mlsanity.com/paper/1408.0173/) (2014)

## Built on by (most cited first, 8 of 8)

- [Aberration-Aware Depth-from-Focus](https://mlsanity.com/paper/2303.04654/) (2023)
- [Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Fields Using Sharpness Prior](https://mlsanity.com/paper/2401.00825/) (2024)
- [Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field](https://mlsanity.com/paper/2406.07329/) (2024)
- [HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image Priors](https://mlsanity.com/paper/2407.18443/) (2024)
- [Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues](https://mlsanity.com/paper/2505.17358/) (2025)
- [Robust Shape from Focus via Multiscale Directional Dilated Laplacian and Recurrent Network](https://mlsanity.com/paper/2512.10498/) (2025)
- [Generative Multi-Focus Image Fusion](https://mlsanity.com/paper/2512.21495/) (2025)
- [Towards Minimal Focal Stack in Shape from Focus](https://mlsanity.com/paper/2604.01603/) (2026)

## Abstract (arXiv)

For better photography, most recent commercial cameras including smartphones have either adopted large-aperture lens to collect more light or used a burst mode to take multiple images within short times. These interesting features lead us to examine depth from focus/defocus. In this work, we present a convolutional neural network-based depth estimation from single focal stacks. Our method differs from relevant state-of-the-art works with three unique features. First, our method allows depth maps to be inferred in an end-to-end manner even with image alignment. Second, we propose a sharp region detection module to reduce blur ambiguities in subtle focus changes and weakly texture-less regions. Third, we design an effective downsampling module to ease flows of focal information in feature extractions. In addition, for the generalization of the proposed network, we develop a simulator to realistically reproduce the features of commercial cameras, such as changes in field of view, focal length and principal points. By effectively incorporating these three unique features, our network achieves the top rank in the DDFF 12-Scene benchmark on most metrics. We also demonstrate the effectiveness of the proposed method on various quantitative evaluations and real-world images taken from various off-the-shelf cameras compared with state-of-the-art methods. Our source code is publicly available at https://github.com/wcy199705/DfFintheWild.