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https://github.com/choyingw/Awesome-Monocular-Depth

A curated list of recent monocular depth estimation papers
https://github.com/choyingw/Awesome-Monocular-Depth

List: Awesome-Monocular-Depth

3d awesome-list computer-vision cvpr depth diffusion-models eccv iccv iclr monocular-depth-estimation neurips single-view-3d

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A curated list of recent monocular depth estimation papers

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# Awesome Monocular Depth Estimation [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A list of recent monocular depth estimation work, inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision).



The list is mainly focusing on recent work after 2020



## Papers



High Performance 



- [Metric3D v2 A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://jugghm.github.io/Metric3Dv2/) (high-performing metric depth on zero-shot evaluation), arxiv 2024  | [github](https://jugghm.github.io/Metric3Dv2/) 

- [UniDepth: Universal Monocular Metric Depth Estimation](https://github.com/lpiccinelli-eth/unidepth), (universal metric depth estimation; one's zero-shot performance match depth-anything on NYUv2), CVPR 2024  | [github](https://github.com/lpiccinelli-eth/unidepth) 

- [Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://github.com/prs-eth/marigold) (diffusion), CVPR 2024  | [github](https://github.com/prs-eth/marigold) 

- [ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation](https://github.com/aradhye2002/ecodepth) (diffusion), CVPR 2024  | [github](https://github.com/aradhye2002/ecodepth) 

- [Harnessing Diffusion Models for Visual Perception with Meta Prompts](https://github.com/fudan-zvg/meta-prompts) (diffusion), arxiv 2024  | [github](https://github.com/fudan-zvg/meta-prompts) 

- [DepthFM: Fast Monocular Depth Estimation with Flow Matching](https://depthfm.github.io/), (Depth Estimation using Flow Matching), arXiv 2024

- [Unleashing the Power of Large-Scale Unlabeled Data](https://depth-anything.github.io/), CVPR 2024  | [github](https://github.com/LiheYoung/Depth-Anything) [huggingface](https://huggingface.co/spaces/LiheYoung/Depth-Anything)

- [EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment](https://lavreniuk.github.io/EVP/) (Diffusion), arXiv 2023  | [github](https://github.com/Lavreniuk/EVP)

- [Harnessing Diffusion Models for Visual Perception with Meta Prompts](https://github.com/fudan-zvg/meta-prompts?tab=readme-ov-file), arXiv 2023 | [github](https://github.com/fudan-zvg/meta-prompts?tab=readme-ov-file)

- [IEBins: Iterative Elastic Bins for Monocular Depth Estimation](https://github.com/ShuweiShao/IEBins), NeurIPS 2023 | [github](https://github.com/ShuweiShao/IEBins) 

- [Text-Image Alignment for Diffusion-Based Perception](http://www.vision.caltech.edu/tadp/) (Diffusion), CVPR 2024 | [github](https://github.com/damaggu/tadp)

- [Robust Monocular Depth Estimation under Challenging Conditions](https://md4all.github.io/), ICCV 2023 | [github](https://github.com/md4all/md4all)

- [Unleashing Text-to-Image Diffusion Models for Visual Perception](https://vpd.ivg-research.xyz/) (Diffusion), ICCV 2023 | [github](https://github.com/wl-zhao/VPD)

- [Neural Video Depth Stabilizer](https://github.com/raymondwang987/nvds) , ICCV 2023 | [github](https://github.com/raymondwang987/nvds) 

- [The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation](https://diffusion-vision.github.io/) (Diffusion), arXiv 2023 

- [All in Tokens: Unifying Output Space of Visual Tasks via Soft Token](https://github.com/swintransformer/ait) (tokenized), arXiv 2023 | [github](https://github.com/swintransformer/ait) 

- [Revealing the Dark Secrets of Masked Image Modeling](http://www.computationalimaging.org/publications/automatic-integration/) (tokenization approach), CVPR 2023 | [github](https://github.com/SwinTransformer/MIM-Depth-Estimation) 

- [Internal Discretization for Monocular Depth Estimation](https://www.vis.xyz/pub/idisc/) (tokenization approach), CVPR 2023 | [github](https://github.com/SysCV/idisc) 

- [Neural Video Depth Stabilizer](https://raymondwang987.github.io/NVDS/), ICCV 2023 | [github](https://github.com/raymondwang987/nvds) 

- [VA-DepthNet: A Variational Approach to Single Image Depth Prediction](https://github.com/cnexah/va-depthnet), ICLR 2023 | [github](https://github.com/cnexah/va-depthnet) 

- [Improving Deep Regression with Ordinal Entropy](https://github.com/needylove/ordinalentropy), ICLR 2023 | [github](https://github.com/needylove/ordinalentropy) 

- [DDP: Diffusion Model for Dense Visual Prediction](https://github.com/jiyuanfeng/ddp) (Diffusion), arXiv 2023 | [github](https://github.com/jiyuanfeng/ddp) 

- [PixelFormer: Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention](https://github.com/ashutosh1807/pixelformer) (Diffusion), WACV 2023 | [github](https://github.com/ashutosh1807/pixelformer) 

- [LocalBins: Improving Depth Estimation by Learning Local Distributions](https://github.com/shariqfarooq123/localbins), CVPR 2022 | [github](https://github.com/shariqfarooq123/localbins) 

- [NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation](https://weihaosky.github.io/newcrfs/), CVPR 2022 | [github](https://github.com/aliyun/NeWCRFs) 



Self-Supervised Depth Estimation



- [SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation](https://ojs.aaai.org/index.php/AAAI/article/view/28383), AAAI 2024

- [Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models](https://arxiv.org/pdf/2405.11158), arxiv 2024

- [Complete contextual information extraction for self-supervised monocular depth estimation](https://www.sciencedirect.com/science/article/abs/pii/S1077314224001139), CVIU 2024

- [FA-Depth: Toward Fast and Accurate Self-supervised Monocular Depth Estimation](https://arxiv.org/abs/2405.10885), arxiv 2024

- [Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-Supervised Depth Estimation](https://openaccess.thecvf.com/content/ICCV2023/html/Zhou_Two-in-One_Depth_Bridging_the_Gap_Between_Monocular_and_Binocular_Self-Supervised_ICCV_2023_paper.html), ICCV 2023

- [DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium](https://openaccess.thecvf.com/content/CVPR2023/html/Bangunharcana_DualRefine_Self-Supervised_Depth_and_Pose_Estimation_Through_Iterative_Epipolar_Sampling_CVPR_2023_paper.html), CVPR 2023

- [Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem](https://openaccess.thecvf.com/content/WACV2023/html/Chen_Self-Supervised_Monocular_Depth_Estimation_Solving_the_Edge-Fattening_Problem_WACV_2023_paper.html), WACV 2023

- [3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces](https://openaccess.thecvf.com/content/ICCV2023/papers/Shi_3D_Distillation_Improving_Self-Supervised_Monocular_Depth_Estimation_on_Reflective_Surfaces_ICCV_2023_paper.pdf), ICCV 2023

- [GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_GasMono_Geometry-Aided_Self-Supervised_Monocular_Depth_Estimation_for_Indoor_Scenes_ICCV_2023_paper.pdf), ICCV 2023

- [CORE: Co-planarity Regularized Monocular Geometry Estimation with Weak Supervision](https://openaccess.thecvf.com/content/ICCV2023/papers/Li_CORE_Co-planarity_Regularized_Monocular_Geometry_Estimation_with_Weak_Supervision_ICCV_2023_paper.pdf), ICCV 2023

- [Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Lite-Mono_A_Lightweight_CNN_and_Transformer_Architecture_for_Self-Supervised_Monocular_CVPR_2023_paper.html), ICCV 2023 | [github](https://github.com/noahzn/Lite-Mono) 

- [Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimation](https://arxiv.org/abs/2205.11083), AAAI 2023

- [Self-supervised monocular depth estimation with a vision transformer](https://arxiv.org/abs/2208.03543), 3DV 2022 | [github](https://github.com/zxcqlf/MonoViT)

- [Self-Supervised Surround-View Depth Estimation with Volumetric Feature Fusion](https://openreview.net/forum?id=0PfIQs-ttQQ), NeurIPS 2022

- [Devnet: Self-supervised monocular depth learning via density volume construction](https://arxiv.org/abs/2209.06351), ECCV 2022 | [github](https://github.com/kaichen-z/DevNet)

- [Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation](https://openaccess.thecvf.com/content/CVPR2022/papers/Petrovai_Exploiting_Pseudo_Labels_in_a_Self-Supervised_Learning_Framework_for_Improved_CVPR_2022_paper.pdf), ECCV 2022

- [RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation](https://arxiv.org/abs/2207.11984), ECCV 2022 | [github](https://github.com/hmhemu/RA-Depth)

- [Toward Practical Monocular Indoor Depth Estimation](https://distdepth.github.io/), CVPR 2022 | [github](https://github.com/facebookresearch/DistDepth)

- [Multi-Frame Self-Supervised Depth with Transformers](https://arxiv.org/abs/2204.07616), CVPR 2022



Metric Depth from Single Image



- [Unleashing the Power of Large-Scale Unlabeled Data](https://depth-anything.github.io/), CVPR 2024  | [github](https://github.com/LiheYoung/Depth-Anything) [huggingface](https://huggingface.co/spaces/LiheYoung/Depth-Anything)

- [Metric3D v2 A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://jugghm.github.io/Metric3Dv2/) (high-performing metric depth on zero-shot evaluation), arxiv 2024  | [github](https://jugghm.github.io/Metric3Dv2/) 

- [UniDepth: Universal Monocular Metric Depth Estimation](https://github.com/lpiccinelli-eth/unidepth), (universal metric depth estimation; one's zero-shot performance match depth-anything on NYUv2), CVPR 2024  | [github](https://github.com/lpiccinelli-eth/unidepth) 

- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://github.com/isl-org/ZoeDepth), arXiv 2023 | [github](https://github.com/isl-org/ZoeDepth)

- [Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image](https://github.com/YvanYin/Metric3D), ICCV 2023 | [github](https://github.com/YvanYin/Metric3D)

- [Towards Zero-Shot Scale-Aware Monocular Depth Estimation](https://sites.google.com/view/tri-zerodepth), ICCV 2023

- [Toward Practical Monocular Indoor Depth Estimation](https://distdepth.github.io/), CVPR 2022 | [github](https://github.com/facebookresearch/DistDepth)



Depth for Non-Lambertain Surface



- [Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces](https://arxiv.org/abs/2301.08245), TPAMI

- [3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces](https://openaccess.thecvf.com/content/ICCV2023/papers/Shi_3D_Distillation_Improving_Self-Supervised_Monocular_Depth_Estimation_on_Reflective_Surfaces_ICCV_2023_paper.pdf), ICCV 2023

- [Mirror3D: Depth Refinement for Mirror Surfaces](https://3dlg-hcvc.github.io/mirror3d), CVPR 2021



Depth from Dual-Pixel, optics, or photography



- [Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography](https://github.com/princeton-computational-imaging/SoaP), CVPR 2023 | [github](https://github.com/princeton-computational-imaging/SoaP)

- [Fully Self-Supervised Depth Estimation from Defocus Clue](https://github.com/Ehzoahis/DEReD), CVPR 2023 | [github](https://github.com/Ehzoahis/DEReD)

- [Calibration-free deep optics for depth estimation with precise simulation](https://www.sciencedirect.com/science/article/abs/pii/S0143816624002926), Optics and Lasers in Engineering 2024

- [Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels](https://augmentedperception.github.io/du2net/), ECCV 2022

- [Dual pixel exploration: Simultaneous depth estimation and image restoration](https://github.com/panpanfei/Dual-Pixel-Exploration-Simultaneous-Depth-Estimation-and-Image-Restoration), CVPR 2021 | [github](https://github.com/panpanfei/Dual-Pixel-Exploration-Simultaneous-Depth-Estimation-and-Image-Restoration)

- [Learning single camera depth estimation using dual-pixels](https://github.com/google-research/google-research/blob/master/dual_pixels/README.md), ICCV 2019 | [github](https://github.com/google-research/google-research/blob/master/dual_pixels/README.md)

- [Modeling Defocus-Disparity in Dual-Pixel Sensors](https://github.com/abhijithpunnappurath/dual-pixel-defocus-disparity), ICCP 2020 | [github](https://github.com/abhijithpunnappurath/dual-pixel-defocus-disparity)



Fisheye



- [SlaBins: Fisheye Depth Estimation using Slanted Bins on Road Environments](https://syniez.github.io/SlaBins/), ICCV 2023

- [SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving](https://github.com/valeoai/WoodScape), RAL 2021 | [github](https://github.com/valeoai/WoodScape)



360 deg Depth



- [CRF360D: Monocular 360 Depth Estimation via Neural Spherical Fully-Connected CRFs](https://vlislab22.github.io/CRF360D/), arxiv 2024

- [Distortion-Aware Self-Supervised Indoor 360∘ Depth Estimation via Hybrid Projection Fusion and Structural Regularities](https://arxiv.org/abs/2204.01027), TMM 2023

- [PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation](https://arxiv.org/abs/2203.09283), ECCV 2022 | [github](https://github.com/zhijieshen-bjtu/PanoFormer)

- [Omnifusion: 360 monocular depth estimation via geometry-aware fusion](https://arxiv.org/abs/2203.00838), CVPR 2022 | [github](https://github.com/yuyanli0831/OmniFusion)



Sparse Depth Completion



- [LRRU: Long-short Range Recurrent Updating Networks for Depth Completion](https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_LRRU_Long-short_Range_Recurrent_Updating_Networks_for_Depth_Completion_ICCV_2023_paper.pdf), ICCV 2023 | [github](https://github.com/YufeiWang777/LRRU) 

- [BEV@DC: Bird's-Eye View Assisted Training for Depth Completion](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_BEVDC_Birds-Eye_View_Assisted_Training_for_Depth_Completion_CVPR_2023_paper.pdf), CVPR 2023 

- [MFF-Net: Towards Efficient Monocular Depth Completion With Multi-Modal Feature Fusion](https://ieeexplore.ieee.org/abstract/document/10008014), RAL 2023

- [CompletionFormer: Depth Completion with Convolutions and Vision Transformers](https://arxiv.org/pdf/2304.13030.pdf), CVPR 2023 | [github](https://github.com/youmi-zym/CompletionFormer)

- [Dynamic Spatial Propagation Network for Depth Completion](https://arxiv.org/pdf/2202.09769.pdf), AAAI 2022 | [github](https://github.com/Kyakaka/DySPN)  

- [CostDCNet: Cost Volume based Depth Completion for a Single RGB-D Image](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136620248.pdf), ECCV 2022 | [github](https://github.com/kamse/CostDCNet) 

- [Monitored Distillation for Positive Congruent Depth Completion](https://arxiv.org/pdf/2203.16034.pdf), ECCV 2022 | [github](https://github.com/alexklwong/calibrated-backprojection-network) 

- [RigNet: Repetitive Image Guided Network for Depth Completion](https://arxiv.org/abs/2107.13802), ECCV 2022

- [PENet: Towards Precise and Efficient Image Guided Depth Completion](https://arxiv.org/abs/2103.00783), ICRA 2021 | [github](https://github.com/JUGGHM/PENet_ICRA2021) 

- [Scene Completeness-Aware Lidar Depth Completion for Driving Scenario](https://arxiv.org/abs/2003.06945), ICASSP 2021 | [github](https://github.com/choyingw/SCADC-DepthCompletion)

- [Non-Local Spatial Propagation Network for Depth Completion](https://arxiv.org/pdf/2007.10042.pdf), ECCV 2020 | [github](https://github.com/zzangjinsun/NLSPN_ECCV20) 

- [Depth Completion From Sparse LiDAR Data With Depth-Normal Constraints](https://arxiv.org/abs/1910.06727), ICCV 2019 | [github](https://github.com/yuyanli0831/OmniFusion)

- [DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion](https://arxiv.org/pdf/1902.00761), ITSC 2019 | [github](https://github.com/ShreyasSkandanS/DFuseNet)

- [Deep RGB-D canonical correlation analysis for sparse depth completion](https://arxiv.org/abs/1906.08967), NeurIPS 2019 | [github](https://github.com/choyingw/CFCNet)



Indoor Datasets



Indoor dataset with a focus on space type

- [InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation](https://arxiv.org/abs/2309.13516), CoRL 2023 | [Data Page](https://github.com/DepthComputation/InSpaceType_Benchmark)

- [Toward practical monocular indoor depth estimation](https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Toward_Practical_Monocular_Indoor_Depth_Estimation_CVPR_2022_paper.pdf), CVPR 2022 | [Data Page](https://github.com/DepthComputation/InSpaceType_Benchmark)