# Single Image Depth Estimation Trained via Depth from Defocus Cues

arXiv: https://arxiv.org/abs/2001.05036 · HTML: https://mlsanity.com/paper/2001.05036/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 30 of 45)

- [Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth](https://mlsanity.com/paper/2203.15174/) (2022)
- [Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models](https://mlsanity.com/paper/2106.04180/) (2021)
- [Single Image Depth Estimation: An Overview](https://mlsanity.com/paper/2104.06456/) (2021)
- [PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes](https://mlsanity.com/paper/2210.01612/) (2022)
- [PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss](https://mlsanity.com/paper/2103.07362/) (2021)
- [OmniControlNet: Dual-stage Integration for Conditional Image Generation](https://mlsanity.com/paper/2406.05871/) (2024)
- [Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision](https://mlsanity.com/paper/2108.10843/) (2021)
- [Fully Self-Supervised Depth Estimation from Defocus Clue](https://mlsanity.com/paper/2303.10752/) (2023)
- [AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields](https://mlsanity.com/paper/2206.06100/) (2022)
- [Aberration-Aware Depth-from-Focus](https://mlsanity.com/paper/2303.04654/) (2023)
- [Learning Depth from Focus in the Wild](https://mlsanity.com/paper/2207.09658/) (2022)
- [On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation](https://mlsanity.com/paper/2109.06163/) (2021)
- [Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance](https://mlsanity.com/paper/2202.13055/) (2022)
- [Depth Estimation and Image Restoration by Deep Learning from Defocused Images](https://mlsanity.com/paper/2302.10730/) (2023)
- [METER: a mobile vision transformer architecture for monocular depth estimation](https://mlsanity.com/paper/2403.08368/) (2024)
- [Camera-Independent Single Image Depth Estimation from Defocus Blur](https://mlsanity.com/paper/2311.13045/) (2023)
- [Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks](https://mlsanity.com/paper/2106.13041/) (2021)
- [DDCNet: Deep Dilated Convolutional Neural Network for Dense Prediction](https://mlsanity.com/paper/2107.04715/) (2021)
- [WorldGen: A Large Scale Generative Simulator](https://mlsanity.com/paper/2210.00715/) (2022)
- [Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur](https://mlsanity.com/paper/2507.00372/) (2025)
- [Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus](https://mlsanity.com/paper/2409.12323/) (2024)
- [Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues](https://mlsanity.com/paper/2505.17358/) (2025)
- [A Novel 3D-UNet Deep Learning Framework Based on High-Dimensional Bilateral Grid for Edge Consistent Single Image Depth Estimation](https://mlsanity.com/paper/2105.10129/) (2021)
- [Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos](https://mlsanity.com/paper/2205.08851/) (2022)
- [DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus](https://mlsanity.com/paper/2211.10984/) (2022)
- [Depth from Coupled Optical Differentiation](https://mlsanity.com/paper/2409.10725/) (2024)
- [FocDepthFormer: Transformer with latent LSTM for Depth Estimation from Focal Stack](https://mlsanity.com/paper/2310.11178/) (2023)
- [Learning Monocular Depth from Focus with Event Focal Stack](https://mlsanity.com/paper/2405.06944/) (2024)
- [DDCNet-Multires: Effective Receptive Field Guided Multiresolution CNN for Dense Prediction](https://mlsanity.com/paper/2107.05634/) (2021)
- [Point Spread Function Estimation of Defocus](https://mlsanity.com/paper/2203.02953/) (2022)

## Abstract (arXiv)

Estimating depth from a single RGB images is a fundamental task in computer vision, which is most directly solved using supervised deep learning. In the field of unsupervised learning of depth from a single RGB image, depth is not given explicitly. Existing work in the field receives either a stereo pair, a monocular video, or multiple views, and, using losses that are based on structure-from-motion, trains a depth estimation network. In this work, we rely, instead of different views, on depth from focus cues. Learning is based on a novel Point Spread Function convolutional layer, which applies location specific kernels that arise from the Circle-Of-Confusion in each image location. We evaluate our method on data derived from five common datasets for depth estimation and lightfield images, and present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches. Since the phenomenon of depth from defocus is not dataset specific, we hypothesize that learning based on it would overfit less to the specific content in each dataset. Our experiments show that this is indeed the case, and an estimator learned on one dataset using our method provides better results on other datasets, than the directly supervised methods.