mlsanityData to 7 Oct 2026

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

arXiv 1406.2283 ↗First posted 9 Jun 2014cs.CVcited by 1,264

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Cited by 74 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation.

Key facts. Cited by 1,264 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2014.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 225; 2022: 213; 2023: 224; 2024: 226; 2025: 200; 2026: 176.

1,264 papers.

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  1. 2021Vision Transformers for Dense Predictioncited by 1,789
  2. 2024Depth Anything V2cited by 1,385
  3. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,346
  4. 2023Scaling Vision Transformers to 22 Billion Parameterscited by 690
  5. 2023pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstructioncited by 668
  6. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 646
  7. 2025Depth Anything 3: Recovering the Visual Space from Any Viewscited by 626
  8. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 568
  9. 2021Neural Fields in Visual Computing and Beyondcited by 539
  10. 2022MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstructioncited by 467
  11. 2024Depth Pro: Sharp Monocular Metric Depth in Less Than a Secondcited by 395
  12. 2024Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimationcited by 389
38 more
  1. 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 375
  2. 2023Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Imagecited by 357
  3. 2023Repurposing Diffusion-Based Image Generators for Monocular Depth Estimationcited by 323
  4. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 322
  5. 2024MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervisioncited by 290
  6. 2024DepthSplat: Connecting Gaussian Splatting and Depthcited by 275
  7. 2023Unleashing Text-to-Image Diffusion Models for Visual Perceptioncited by 266
  8. 2022Cross-view Transformers for real-time Map-view Semantic Segmentationcited by 264
  9. 2025MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Detailscited by 250
  10. 2021Is Pseudo-Lidar needed for Monocular 3D Object detection?cited by 248
  11. 2023A Cookbook of Self-Supervised Learningcited by 248
  12. 2021NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Videocited by 241
  13. 2024Sapiens: Foundation for Human Vision Modelscited by 229
  14. 2024DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videoscited by 202
  15. 2025Video Depth Anything: Consistent Depth Estimation for Super-Long Videoscited by 202
  16. 2024MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priorscited by 198
  17. 2021NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereocited by 191
  18. 2021The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depthcited by 190
  19. 2021Probabilistic and Geometric Depth: Detecting Objects in Perspectivecited by 190
  20. 2023NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusioncited by 179
  21. 2024Probing the 3D Awareness of Visual Foundation Modelscited by 175
  22. 2025UniDepthV2: Universal Monocular Metric Depth Estimation Made Simplercited by 170
  23. 2022BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimationcited by 166
  24. 2024Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Predictioncited by 163
  25. 2023SceneScape: Text-Driven Consistent Scene Generationcited by 160
  26. 2021Delving into Deep Imbalanced Regressioncited by 157
  27. 2022NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimationcited by 151
  28. 2023DDP: Diffusion Model for Dense Visual Predictioncited by 148
  29. 2021PENet: Towards Precise and Efficient Image Guided Depth Completioncited by 146
  30. 2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
  31. 2021MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesiscited by 133
  32. 2023RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervisioncited by 126
  33. 2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
  34. 2023DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Modelscited by 122
  35. 2024Rethinking Inductive Biases for Surface Normal Estimationcited by 122
  36. 2022Revealing the Dark Secrets of Masked Image Modelingcited by 117
  37. 2022DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimationcited by 116
  38. 2023The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimationcited by 116

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