mlsanityData to 14 Oct 2026

The Monocular Depth Estimation Challenge

arXiv 2211.12174 ↗First posted 22 Nov 2022cs.CVcited by 13

Not on the map. Why?

Abstract (arXiv)

This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset. The challenge was organized on CodaLab and received submissions from 4 valid teams. Participants were provided a devkit containing updated reference implementations for 16 State-of-the-Art algorithms and 4 novel techniques. The threshold for acceptance for novel techniques was to outperform every one of the 16 SotA baselines. All participants outperformed the baseline in traditional metrics such as MAE or AbsRel. However, pointcloud reconstruction metrics were challenging to improve upon. We found predictions were characterized by interpolation artefacts at object boundaries and errors in relative object positioning. We hope this challenge is a valuable contribution to the community and encourage authors to participate in future editions.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 51 of its 68 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

51 of 68 references.

References · 51

  1. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  2. 2022A ConvNet for the 2020scited by 3,579
  3. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  4. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  5. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  6. 2015Spatial Transformer Networkscited by 1,429
  7. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
  8. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  9. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  10. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  11. 2018Pyramid Stereo Matching Networkcited by 489
  12. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
38 more
  1. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 439
  2. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  3. 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 353
  4. 2017Sparsity Invariant CNNscited by 326
  5. 2019Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Videocited by 244
  6. 2020Image Matching across Wide Baselines: From Paper to Practicecited by 225
  7. 2020D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometrycited by 206
  8. 2021Pixel-Perfect Structure-from-Motion with Featuremetric Refinementcited by 200
  9. 2018Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraintscited by 179
  10. 2018Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstructioncited by 159
  11. 2017Learning Depth from Monocular Videos using Direct Methodscited by 154
  12. 2020Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidancecited by 148
  13. 2020Feature-metric Loss for Self-supervised Learning of Depth and Egomotioncited by 140
  14. 2018Evaluation of CNN-based Single-Image Depth Estimation Methodscited by 138
  15. 2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
  16. 2019Self-Supervised Monocular Depth Hintscited by 127
  17. 2021MPViT: Multi-Path Vision Transformer for Dense Predictioncited by 122
  18. 2020Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volumecited by 116
  19. 2020HR-Depth: High Resolution Self-Supervised Monocular Depth Estimationcited by 115
  20. 2017Semi-Supervised Deep Learning for Monocular Depth Map Predictioncited by 109
  21. 2018Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometrycited by 85
  22. 2020Unsupervised Monocular Depth Learning in Dynamic Scenescited by 84
  23. 2021Self-Supervised Monocular Depth Estimation with Internal Feature Fusioncited by 67
  24. 2018SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimationcited by 64
  25. 2019Learning monocular depth estimation infusing traditional stereo knowledgecited by 64
  26. 2021Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimationcited by 63
  27. 2020DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learningcited by 60
  28. 2021Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistencycited by 58
  29. 2020Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumescited by 50
  30. 2019Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimationcited by 49
  31. 2018Unsupervised Adversarial Depth Estimation using Cycled Generative Networkscited by 45
  32. 2021Self-supervised Monocular Depth Estimation for All Day Images using Domain Separationcited by 44
  33. 2018Single View Stereo Matchingcited by 39
  34. 2021Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completioncited by 39
  35. 2018Learning monocular depth estimation with unsupervised trinocular assumptionscited by 36
  36. 2021PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Losscited by 31
  37. 2022From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Predictioncited by 19
  38. 2022Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Mattercited by 18

50 most cited of 51 All 51 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 3; 2026: 1.

13 papers.

Sort:

By area:

  1. 2024Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditionscited by 29
  2. 2023Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTVcited by 21
  3. 2023The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimationcited by 13
  4. 2023The Second Monocular Depth Estimation Challengecited by 12
  5. 2024The Third Monocular Depth Estimation Challengecited by 10
  6. 2025The Fourth Monocular Depth Estimation Challengecited by 8
  7. 2024Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTVcited by 7
  8. 2024Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensationcited by 4
  9. 2025NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfacescited by 3
  10. 2025ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Drivingcited by 1
  11. 2026ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Devicecited by 1
  12. 2024Shape2.5D: A Dataset of Texture-less Surfaces for Depth and Normals Estimationcited by 0
1 more
  1. 2024EDADepth: Enhanced Data Augmentation for Monocular Depth Estimationcited by 0