# Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2406.19675 · HTML: https://mlsanity.com/paper/2406.19675/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 112 of its 194 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 30 of 112)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [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)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://mlsanity.com/paper/1908.10553/) (2019)
- [Consistent Video Depth Estimation](https://mlsanity.com/paper/2004.15021/) (2020)
- [DeMoN: Depth and Motion Network for Learning Monocular Stereo](https://mlsanity.com/paper/1612.02401/) (2016)
- [Proxy Anchor Loss for Deep Metric Learning](https://mlsanity.com/paper/2003.13911/) (2020)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://mlsanity.com/paper/2104.14540/) (2021)
- [Robust Consistent Video Depth Estimation](https://mlsanity.com/paper/2012.05901/) (2020)
- [Single-Image Depth Perception in the Wild](https://mlsanity.com/paper/1604.03901/) (2016)

## Built on by (most cited first, 15 of 15)

- [UniFuture: A 4D Driving World Model for Future Generation and Perception](https://mlsanity.com/paper/2503.13587/) (2025)
- [Depth Anything at Any Condition](https://mlsanity.com/paper/2507.01634/) (2025)
- [MonoTher-Depth: Enhancing Thermal Depth Estimation via Confidence-Aware Distillation](https://mlsanity.com/paper/2504.16127/) (2025)
- [StreamTTO: Efficient Online Test-Time Optimization for Video Depth Completion](https://mlsanity.com/paper/2603.01765/) (2026)
- [FlyAwareV2: A Multimodal Cross-Domain UAV Dataset for Urban Scene Understanding](https://mlsanity.com/paper/2510.13243/) (2025)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation](https://mlsanity.com/paper/2604.09352/) (2026)
- [Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography](https://mlsanity.com/paper/2604.12239/) (2026)
- [SS3D: End2End Self-Supervised 3D from Web Videos](https://mlsanity.com/paper/2604.22686/) (2026)
- [PhysDepth: Plug-and-Play Physical Refinement for Monocular Depth Estimation in Challenging Environments](https://mlsanity.com/paper/2412.04666/) (2024)
- [Survey on Monocular Metric Depth Estimation](https://mlsanity.com/paper/2501.11841/) (2025)
- [MT-Depth: Multi-task Instance feature analysis for the Depth Completion](https://mlsanity.com/paper/2512.04734/) (2025)
- [Accuracy Does Not Guarantee Human-Likeness: Cross-Domain Human-Centered Benchmark in Monocular Depth Estimation](https://mlsanity.com/paper/2512.08163/) (2025)
- [Multi-scale Attention-Guided Intrinsic Decomposition and Rendering Pass Prediction for Facial Images](https://mlsanity.com/paper/2512.16511/) (2025)
- [Learning Vision-Based Omnidirectional Navigation: A Teacher-Student Approach Using Monocular Depth Estimation](https://mlsanity.com/paper/2603.01999/) (2026)

## Abstract (arXiv)

Estimating depth from single RGB images and videos is of widespread interest due to its applications in many areas, including autonomous driving, 3D reconstruction, digital entertainment, and robotics. More than 500 deep learning-based papers have been published in the past 10 years, which indicates the growing interest in the task. This paper presents a comprehensive survey of the existing deep learning-based methods, the challenges they address, and how they have evolved in their architecture and supervision methods. It provides a taxonomy for classifying the current work based on their input and output modalities, network architectures, and learning methods. It also discusses the major milestones in the history of monocular depth estimation, and different pipelines, datasets, and evaluation metrics used in existing methods.