# Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications

arXiv: https://arxiv.org/abs/2011.04123 · HTML: https://mlsanity.com/paper/2011.04123/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR](https://mlsanity.com/paper/2109.09628/) (2021)
- [GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping](https://mlsanity.com/paper/2205.01656/) (2022)
- [HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images](https://mlsanity.com/paper/2310.07995/) (2023)
- [Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset](https://mlsanity.com/paper/2109.07577/) (2021)
- [A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation](https://mlsanity.com/paper/2404.17335/) (2024)
- [Lightweight Monocular Depth Estimation](https://mlsanity.com/paper/2212.11363/) (2022)

## Abstract (arXiv)

Estimating depth from RGB images can facilitate many computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recently, monocular depth estimation has obtained great progress owing to the rapid development of deep learning techniques. They surpass traditional machine learning-based methods by a large margin in terms of accuracy and speed. Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions. In this survey, we first introduce the datasets for depth estimation, and then give a comprehensive introduction of the methods from three perspectives: supervised learning-based methods, unsupervised learning-based methods, and sparse samples guidance-based methods. In addition, downstream applications that benefit from the progress have also been illustrated. Finally, we point out the future directions and conclude the paper.