mlsanityData to 14 Oct 2026

Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation

arXiv 2407.14126 ↗First posted 19 Jul 2024cs.CVcited by 9

Not on the map. Why?

Abstract (arXiv)

Self-supervised monocular depth estimation has gathered notable interest since it can liberate training from dependency on depth annotations. In monocular video training case, recent methods only conduct view synthesis between existing camera views, leading to insufficient guidance. To tackle this, we try to synthesize more virtual camera views by flow-based video frame interpolation (VFI), termed as temporal augmentation. For multi-frame inference, to sidestep the problem of dynamic objects encountered by explicit geometry-based methods like ManyDepth, we return to the feature fusion paradigm and design a VFI-assisted multi-frame fusion module to align and aggregate multi-frame features, using motion and occlusion information obtained by the flow-based VFI model. Finally, we construct a unified self-supervised learning framework, named Mono-ViFI, to bilaterally connect single- and multi-frame depth. In this framework, spatial data augmentation through image affine transformation is incorporated for data diversity, along with a triplet depth consistency loss for regularization. The single- and multi-frame models can share weights, making our framework compact and memory-efficient. Extensive experiments demonstrate that our method can bring significant improvements to current advanced architectures. Source code is available at https://github.com/LiuJF1226/Mono-ViFI.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 56 of its 70 references that are in the map. First posted on arXiv 19 Jul 2024.

Builds on

56 of 70 references.

References · 56

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  5. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  6. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  7. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  8. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
  9. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  10. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 439
  11. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  12. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
38 more
  1. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 342
  2. 2017Sparsity Invariant CNNscited by 326
  3. 2020BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyondcited by 304
  4. 2019Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Videocited by 244
  5. 2018Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentationcited by 222
  6. 2020Consistent Video Depth Estimationcited by 220
  7. 2020Real-Time Intermediate Flow Estimation for Video Frame Interpolationcited by 213
  8. 2021The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depthcited by 190
  9. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
  10. 2023AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolationcited by 180
  11. 2019Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Camerascited by 170
  12. 2017Learning Depth from Monocular Videos using Direct Methodscited by 154
  13. 2020Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidancecited by 148
  14. 2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
  15. 2019Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Cameracited by 130
  16. 2019Self-Supervised Monocular Depth Hintscited by 127
  17. 2020HR-Depth: High Resolution Self-Supervised Monocular Depth Estimationcited by 115
  18. 2022IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolationcited by 109
  19. 2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
  20. 2020Unsupervised Monocular Depth Learning in Dynamic Scenescited by 84
  21. 2020Don't Forget The Past: Recurrent Depth Estimation from Monocular Videocited by 71
  22. 2021Self-Supervised Monocular Depth Estimation with Internal Feature Fusioncited by 67
  23. 2022Multi-Frame Self-Supervised Depth with Transformerscited by 64
  24. 2019Exploiting temporal consistency for real-time video depth estimationcited by 61
  25. 2021Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistencycited by 58
  26. 2021Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimationcited by 55
  27. 2019Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depthcited by 50
  28. 2020Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumescited by 50
  29. 2019Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimationcited by 49
  30. 2021Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimationcited by 48
  31. 2022Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depthcited by 48
  32. 2022RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimationcited by 36
  33. 2022PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planescited by 32
  34. 2021Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimationcited by 27
  35. 2021Attentive and Contrastive Learning for Joint Depth and Motion Field Estimationcited by 24
  36. 2022Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimationcited by 24
  37. 2022DevNet: Self-supervised Monocular Depth Learning via Density Volume Constructioncited by 22
  38. 2023DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibriumcited by 16

50 most cited of 56 All 56 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 3.

9 papers.

  1. 2025Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimationcited by 21
  2. 2025GaussHDR: High Dynamic Range Gaussian Splatting via Learning Unified 3D and 2D Local Tone Mappingcited by 9
  3. 2025Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimationcited by 6
  4. 2025Mono4DGS-HDR: High Dynamic Range 4D Gaussian Splatting from Alternating-exposure Monocular Videoscited by 2
  5. 2024D3epth: Self-Supervised Depth Estimation with Dynamic Mask in Dynamic Scenescited by 1
  6. 2025CylinderDepth: Cylindrical Spatial Attention for Multi-View Consistent Self-Supervised Surround Depth Estimationcited by 1
  7. 2026PuriLight: A Lightweight Shuffle and Purification Framework for Monocular Depth Estimationcited by 1
  8. 2026SA4Depth: Consistent Pose-Depth Scale Alignment for Self-Supervised Monocular Depth Estimationcited by 0
  9. 2026CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimationcited by 0