mlsanityData to 14 Oct 2026

MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

arXiv 2311.07198 ↗First posted 13 Nov 2023cs.CVcited by 13

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

Over the past few years, self-supervised monocular depth estimation that does not depend on ground-truth during the training phase has received widespread attention. Most efforts focus on designing different types of network architectures and loss functions or handling edge cases, e.g., occlusion and dynamic objects. In this work, we introduce a novel self-supervised depth estimation framework, dubbed MonoDiffusion, by formulating it as an iterative denoising process. Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion. The pseudo ground-truth diffusion gradually adds noise to the depth map generated by a pre-trained teacher model. Moreover,the teacher model allows applying a distillation loss to guide the denoised depth. Further, we develop a masked visual condition mechanism to enhance the denoising ability of model. Extensive experiments are conducted on the KITTI and Make3D datasets and the proposed MonoDiffusion outperforms prior state-of-the-art competitors. The source code will be available at https://github.com/ShuweiShao/MonoDiffusion.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 80 of its 100 references that are in the map. First posted on arXiv 13 Nov 2023.

Builds on

80 of 100 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation2 Jun
  2. 2023Monocular Depth Estimation using Diffusion Models28 Feb
  3. 2022DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models29 Nov

Other references · 77

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  7. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  8. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  9. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  10. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  11. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  12. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
38 more
  1. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  2. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  3. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  4. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
  5. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  6. 2015Spatial Transformer Networkscited by 1,429
  7. 2021Palette: Image-to-Image Diffusion Modelscited by 1,300
  8. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  9. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
  10. 2022Human Motion Diffusion Modelcited by 1,080
  11. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  12. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  13. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  14. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
  15. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 657
  16. 2020AdaBins: Depth Estimation using Adaptive Binscited by 601
  17. 2021Label-Efficient Semantic Segmentation with Diffusion Modelscited by 499
  18. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 439
  19. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  20. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
  21. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  22. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
  23. 2017Sparsity Invariant CNNscited by 326
  24. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 280
  25. 2023MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizercited by 262
  26. 2019Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Videocited by 244
  27. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 236
  28. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 234
  29. 2018Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentationcited by 222
  30. 2020D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometrycited by 206
  31. 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
  32. 2021Conditional Image Generation with Score-Based Diffusion Modelscited by 197
  33. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Modelcited by 180
  34. 2017Learning Depth from Monocular Videos using Direct Methodscited by 154
  35. 2022NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimationcited by 153
  36. 2018DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistencycited by 148
  37. 2020Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidancecited by 148
  38. 2023DDP: Diffusion Model for Dense Visual Predictioncited by 148

50 most cited of 77 All 77 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 3.

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  1. 2025Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimationcited by 21
  2. 2025DepthMaster: Taming Diffusion Models for Monocular Depth Estimationcited by 19
  3. 2024Distillation of Diffusion Features for Semantic Correspondencecited by 17
  4. 2025From Image to Video: An Empirical Study of Diffusion Representationscited by 15
  5. 2024DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimationcited by 13
  6. 2024Multi-scale Latent Point Consistency Models for 3D Shape Generationcited by 7
  7. 2025Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimationcited by 6
  8. 2024Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspectivecited by 4
  9. 2025Discriminately Treating Motion Components Evolves Joint Depth and Ego-Motion Learningcited by 1
  10. 2025Survey on Monocular Metric Depth Estimationcited by 0
  11. 2026Correlation Analysis of Generative Modelscited by 0
  12. 2026CDPR: Cross-modal Diffusion with Polarization for Reliable Monocular Depth Estimationcited by 0
1 more
  1. 2026Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimationcited by 0

Similar, not cited

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  1. 2024Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions
  2. 2026Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation
  3. 2024Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion
  4. 2023MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty
  5. 2024PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage
  6. 2024Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation
  7. 2023Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
  8. 2021SUB-Depth: Self-distillation and Uncertainty Boosting Self-supervised Monocular Depth Estimation