mlsanityData to 7 Oct 2026

Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation

arXiv 2211.13202 ↗First posted 23 Nov 2022cs.CVcited by 100

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Abstract (arXiv)

Self-supervised monocular depth estimation that does not require ground truth for training has attracted attention in recent years. It is of high interest to design lightweight but effective models so that they can be deployed on edge devices. Many existing architectures benefit from using heavier backbones at the expense of model sizes. This paper achieves comparable results with a lightweight architecture. Specifically, the efficient combination of CNNs and Transformers is investigated, and a hybrid architecture called Lite-Mono is presented. A Consecutive Dilated Convolutions (CDC) module and a Local-Global Features Interaction (LGFI) module are proposed. The former is used to extract rich multi-scale local features, and the latter takes advantage of the self-attention mechanism to encode long-range global information into the features. Experiments demonstrate that Lite-Mono outperforms Monodepth2 by a large margin in accuracy, with about 80% fewer trainable parameters.

Key facts. Cited by 100 papers in the map (data through 7 Oct 2026). Builds on 36 of its 47 references that are in the map. First posted on arXiv 23 Nov 2022.

Builds on

36 of 47 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2020End-to-End Object Detection with Transformerscited by 7,384
  5. 2016Layer Normalizationcited by 5,579
  6. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  7. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  8. 2021Vision Transformers for Dense Predictioncited by 1,789
  9. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  10. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
  11. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  12. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
24 more
  1. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 778
  2. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  3. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  4. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 402
  5. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  6. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 341
  7. 2021XCiT: Cross-Covariance Image Transformerscited by 335
  8. 2021CMT: Convolutional Neural Networks Meet Vision Transformerscited by 310
  9. 2020D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometrycited by 206
  10. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
  11. 2015Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fieldscited by 169
  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. 2021MPViT: Multi-Path Vision Transformer for Dense Predictioncited by 122
  16. 2022EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applicationscited by 121
  17. 2020Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volumecited by 116
  18. 2020HR-Depth: High Resolution Self-Supervised Monocular Depth Estimationcited by 115
  19. 2017Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimationcited by 70
  20. 2021Self-Supervised Monocular Depth Estimation with Internal Feature Fusioncited by 67
  21. 2021Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimationcited by 63
  22. 2021Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimationcited by 55
  23. 2019Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semanticscited by 49
  24. 2022Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsicscited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 16; 2024: 45; 2025: 21; 2026: 18.

100 papers.

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  1. 2023SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Drivingcited by 72
  2. 2023RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptionscited by 47
  3. 2024Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesiscited by 41
  4. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  5. 2023Self-supervised Monocular Depth Estimation: Let's Talk About The Weathercited by 33
  6. 2023Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenescited by 20
  7. 2025Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimationcited by 20
  8. 2025Synthetic-to-Real Self-supervised Robust Depth Estimation via Learning with Motion and Structure Priorscited by 20
  9. 2024MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densificationcited by 16
  10. 2023AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimationcited by 15
  11. 2023Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Networkcited by 14
  12. 2023The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimationcited by 13
38 more
  1. 2023MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Modelcited by 12
  2. 2024DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimationcited by 12
  3. 2024MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Imagescited by 10
  4. 2024M2Depth: Self-supervised Two-Frame Multi-camera Metric Depth Estimationcited by 10
  5. 2024Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimationcited by 9
  6. 2024Embodiment: Self-Supervised Depth Estimation Based on Camera Modelscited by 9
  7. 2025Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokenscited by 9
  8. 2024FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Cameracited by 8
  9. 2024ViPOcc: Leveraging Visual Priors from Vision Foundation Models for Single-View 3D Occupancy Predictioncited by 8
  10. 2024Towards Cross-View-Consistent Self-Supervised Surround Depth Estimationcited by 7
  11. 2024SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learningcited by 7
  12. 2024TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractographycited by 6
  13. 2024ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic Fusioncited by 6
  14. 2024GroCo: Ground Constraint for Metric Self-Supervised Monocular Depthcited by 6
  15. 2025Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimationcited by 6
  16. 2024Scene Prior Filtering for Depth Super-Resolutioncited by 5
  17. 2023AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimizationcited by 4
  18. 2023Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimationcited by 4
  19. 2024Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarioscited by 4
  20. 2024Prototypical Transformer as Unified Motion Learnerscited by 4
  21. 2024MambaDepth: Enhancing Long-range Dependency for Self-Supervised Fine-Structured Monocular Depth Estimationcited by 4
  22. 2024WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimationcited by 4
  23. 2024MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estimation by Planar-Parallax Geometry in Automotive Applicationscited by 4
  24. 2025Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weathercited by 4
  25. 2023BEVScope: Enhancing Self-Supervised Depth Estimation Leveraging Bird's-Eye-View in Dynamic Scenarioscited by 3
  26. 2023Depth Estimation from a Single Optical Encoded Image using a Learned Colored-Coded Aperturecited by 3
  27. 2023Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimationcited by 3
  28. 2024Large Language Models Can Understanding Depth from Monocular Imagescited by 3
  29. 2024Optical Lens Attack on Deep Learning Based Monocular Depth Estimationcited by 3
  30. 2025DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image Enhancementcited by 3
  31. 2023EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformercited by 2
  32. 2024FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimationcited by 2
  33. 2024BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth Estimationcited by 2
  34. 2024High-Precision Self-Supervised Monocular Depth Estimation with Rich-Resource Priorcited by 2
  35. 2024NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingcited by 2
  36. 2025ER-LoRA: Effective-Rank Guided Adaptation for Weather-Generalized Depth Estimationcited by 2
  37. 2025ColonAdapter: Geometry Estimation Through Foundation Model Adaptation for Colonoscopycited by 2
  38. 2023Channel-Aware Distillation Transformer for Depth Estimation on Nano Dronescited by 1

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