mlsanityData to 14 Oct 2026

Outdoor Monocular Depth Estimation: A Research Review

arXiv 2205.01399 ↗First posted 3 May 2022cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Depth estimation is an important task, applied in various methods and applications of computer vision. While the traditional methods of estimating depth are based on depth cues and require specific equipment such as stereo cameras and configuring input according to the approach being used, the focus at the current time is on a single source, or monocular, depth estimation. The recent developments in Convolution Neural Networks along with the integration of classical methods in these deep learning approaches have led to a lot of advancements in the depth estimation problem. The problem of outdoor depth estimation, or depth estimation in wild, is a very scarcely researched field of study. In this paper, we give an overview of the available datasets, depth estimation methods, research work, trends, challenges, and opportunities that exist for open research. To our knowledge, no openly available survey work provides a comprehensive collection of outdoor depth estimation techniques and research scope, making our work an essential contribution for people looking to enter this field of study.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 41 of its 67 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

41 of 67 references.

References · 41

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29 more
  1. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  2. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
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  9. 2018Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Cameracited by 140
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  11. 2014Deep Convolutional Neural Fields for Depth Estimation from a Single Imagecited by 106
  12. 2020ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentationcited by 96
  13. 2018SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimationcited by 64
  14. 2019Exploiting temporal consistency for real-time video depth estimationcited by 61
  15. 2020The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truthcited by 61
  16. 2018Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotationscited by 51
  17. 2019Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depthcited by 50
  18. 2021ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimationcited by 39
  19. 2021PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentationcited by 36
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  23. 2018Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matchingcited by 20
  24. 2022Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsicscited by 19
  25. 2018Interpreting recurrent neural networks behaviour via excitable network attractorscited by 15
  26. 2022360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Networkcited by 15
  27. 2022A Survey on RGB-D Datasetscited by 11
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  29. 2019Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenescited by 5

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2024Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Surveycited by 15
  2. 2023HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decodercited by 1
  3. 2025Endoscopic Depth Estimation Based on Deep Learning: A Surveycited by 1
  4. 2025Survey on Monocular Metric Depth Estimationcited by 0