# A Survey on Deep Learning Architectures for Image-based Depth Reconstruction

arXiv: https://arxiv.org/abs/1906.06113 · HTML: https://mlsanity.com/paper/1906.06113/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation](https://mlsanity.com/paper/2305.15186/) (2023)
- [GLPanoDepth: Global-to-Local Panoramic Depth Estimation](https://mlsanity.com/paper/2202.02796/) (2022)
- [NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis](https://mlsanity.com/paper/2108.03880/) (2021)
- [Traffic-Net: 3D Traffic Monitoring Using a Single Camera](https://mlsanity.com/paper/2109.09165/) (2021)
- [A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images](https://mlsanity.com/paper/2210.14031/) (2022)
- [Fine-tuning deep learning models for stereo matching using results from semi-global matching](https://mlsanity.com/paper/2205.14051/) (2022)
- [Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles](https://mlsanity.com/paper/2310.04837/) (2023)

## Abstract (arXiv)

Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. In this article, we provide a comprehensive survey of the recent developments in this field. We will focus on the works which use deep learning techniques to estimate depth from one or multiple images. Deep learning, coupled with the availability of large training datasets, have revolutionized the way the depth reconstruction problem is being approached by the research community. In this article, we survey more than 100 key contributions that appeared in the past five years, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for learning-based depth reconstruction research.