└── README.md
/README.md:
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1 | # Awesome Monocular Depth Estimation [](https://github.com/sindresorhus/awesome)
2 | A list of recent monocular depth estimation work, inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision).
3 |
4 | The list is mainly focusing on recent work after 2020
5 |
6 | Last update: Oct 2024
7 |
8 | ## Papers
9 |
10 |
11 | High Performance
12 |
13 | - [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073/) (precise focal length estimation with metric depth), arXiv 2024 | [github](https://github.com/apple/ml-depth-pro/)
14 | - [Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization](https://scholar.google.com/scholar?oi=bibs&cluster=10790219912035654526&btnI=1&hl=en) (meta-learning), IROS 2024
15 | - [DoubleTake: Geometry Guided Depth Estimation](https://nianticlabs.github.io/doubletake/resources/DoubleTake.pdf/), ECCV 2024 | [github](https://nianticlabs.github.io/doubletake/)
16 | - [WorDepth: Variational Language Prior for Monocular Depth Estimation](https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_WorDepth_Variational_Language_Prior_for_Monocular_Depth_Estimation_CVPR_2024_paper.html), CVPR 2024 | [github](https://github.com/Adonis-galaxy/WorDepth/)
17 | - [Scale-Invariant Monocular Depth Estimation via SSI Depth](https://dl.acm.org/doi/abs/10.1145/3641519.3657523?casa_token=5BzgjJGb_IkAAAAA:Qh0shrLY5ZvPgVSxomASjA_LDX2vItIIfAo8mRQGbZlGM-DIo-pCwP8dpPLNXQDtgtKd7K_98nEiQg), SIGGRAPH 2024
18 | - [PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation](https://arxiv.org/abs/2312.02284), CVPR 2024 | [github](https://zhyever.github.io/patchfusion/)
19 | - [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/)
20 | - [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)
21 | - [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)
22 | - [ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation](https://github.com/aradhye2002/ecodepth) (diffusion), CVPR 2024 | [github](https://github.com/aradhye2002/ecodepth)
23 | - [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)
24 | - [DepthFM: Fast Monocular Depth Estimation with Flow Matching](https://depthfm.github.io/), (Depth Estimation using Flow Matching), arXiv 2024
25 | - [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)
26 | - [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)
27 | - [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)
28 | - [IEBins: Iterative Elastic Bins for Monocular Depth Estimation](https://github.com/ShuweiShao/IEBins), NeurIPS 2023 | [github](https://github.com/ShuweiShao/IEBins)
29 | - [Text-Image Alignment for Diffusion-Based Perception](http://www.vision.caltech.edu/tadp/) (Diffusion), CVPR 2024 | [github](https://github.com/damaggu/tadp)
30 | - [Robust Monocular Depth Estimation under Challenging Conditions](https://md4all.github.io/), ICCV 2023 | [github](https://github.com/md4all/md4all)
31 | - [Unleashing Text-to-Image Diffusion Models for Visual Perception](https://vpd.ivg-research.xyz/) (Diffusion), ICCV 2023 | [github](https://github.com/wl-zhao/VPD)
32 | - [Neural Video Depth Stabilizer](https://github.com/raymondwang987/nvds) , ICCV 2023 | [github](https://github.com/raymondwang987/nvds)
33 | - [The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation](https://diffusion-vision.github.io/) (Diffusion), NeurIPS 2023
34 | - [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)
35 | - [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)
36 | - [Internal Discretization for Monocular Depth Estimation](https://www.vis.xyz/pub/idisc/) (tokenization approach), CVPR 2023 | [github](https://github.com/SysCV/idisc)
37 | - [Neural Video Depth Stabilizer](https://raymondwang987.github.io/NVDS/), ICCV 2023 | [github](https://github.com/raymondwang987/nvds)
38 | - [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)
39 | - [Improving Deep Regression with Ordinal Entropy](https://github.com/needylove/ordinalentropy), ICLR 2023 | [github](https://github.com/needylove/ordinalentropy)
40 | - [DDP: Diffusion Model for Dense Visual Prediction](https://github.com/jiyuanfeng/ddp) (Diffusion), arXiv 2023 | [github](https://github.com/jiyuanfeng/ddp)
41 | - [PixelFormer: Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention](https://github.com/ashutosh1807/pixelformer) (Diffusion), WACV 2023 | [github](https://github.com/ashutosh1807/pixelformer)
42 | - [LocalBins: Improving Depth Estimation by Learning Local Distributions](https://github.com/shariqfarooq123/localbins), CVPR 2022 | [github](https://github.com/shariqfarooq123/localbins)
43 | - [NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation](https://weihaosky.github.io/newcrfs/), CVPR 2022 | [github](https://github.com/aliyun/NeWCRFs)
44 |
45 |
46 |
47 |
48 | Self-Supervised Depth Estimation
49 |
50 | - [Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation](https://vision.ist.i.kyoto-u.ac.jp/research/fumet/), ECCV 2024
51 | - [SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation](https://ojs.aaai.org/index.php/AAAI/article/view/28383), AAAI 2024 | [github](https://github.com/hisfog/SfMNeXt-Impl)
52 | - [Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models](https://arxiv.org/pdf/2405.11158), ICRA 2024 | [github](https://github.com/madhubabuv/dtd)
53 | - [Complete contextual information extraction for self-supervised monocular depth estimation](https://www.sciencedirect.com/science/article/abs/pii/S1077314224001139), CVIU 2024
54 | - [Mining Supervision for Dynamic Regions in Self-Supervised Monocular
55 | Depth Estimat](https://openaccess.thecvf.com/content/CVPR2024/papers/Nguyen_Mining_Supervision_for_Dynamic_Regions_in_Self-Supervised_Monocular_Depth_Estimation_CVPR_2024_paper.pdf), CVPR 2024 | [github](https://github.com/HoangChuongNguyen/mono-consistent-depth)
56 | - [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
57 | - [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
58 | - [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
59 | - [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
60 | - [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
61 | - [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
62 | - [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)
63 | - [Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimation](https://arxiv.org/abs/2205.11083), AAAI 2023
64 | - [Self-supervised monocular depth estimation with a vision transformer](https://arxiv.org/abs/2208.03543), 3DV 2022 | [github](https://github.com/zxcqlf/MonoViT)
65 | - [Self-Supervised Surround-View Depth Estimation with Volumetric Feature Fusion](https://openreview.net/forum?id=0PfIQs-ttQQ), NeurIPS 2022
66 | - [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)
67 | - [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
68 | - [RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation](https://arxiv.org/abs/2207.11984), ECCV 2022 | [github](https://github.com/hmhemu/RA-Depth)
69 | - [Toward Practical Monocular Indoor Depth Estimation](https://distdepth.github.io/), CVPR 2022 | [github](https://github.com/facebookresearch/DistDepth)
70 | - [Multi-Frame Self-Supervised Depth with Transformers](https://arxiv.org/abs/2204.07616), CVPR 2022
71 |
72 |
73 |
74 | Metric Depth from Single Image
75 |
76 | - [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073/) (precise focal length estimation with metric depth), arXiv 2024 | [github](https://github.com/apple/ml-depth-pro/)
77 | - [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)
78 | - [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/)
79 | - [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)
80 | - [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)
81 | - [Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image](https://github.com/YvanYin/Metric3D), ICCV 2023 | [github](https://github.com/YvanYin/Metric3D)
82 | - [Towards Zero-Shot Scale-Aware Monocular Depth Estimation](https://sites.google.com/view/tri-zerodepth), ICCV 2023
83 | - [Toward Practical Monocular Indoor Depth Estimation](https://distdepth.github.io/), CVPR 2022 | [github](https://github.com/facebookresearch/DistDepth)
84 |
85 |
86 |
87 |
88 | Depth for Non-Lambertain Surface
89 |
90 | - [Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces](https://arxiv.org/abs/2301.08245), TPAMI
91 | - [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
92 | - [Mirror3D: Depth Refinement for Mirror Surfaces](https://3dlg-hcvc.github.io/mirror3d), CVPR 2021
93 |
94 |
95 |
96 | Depth from Dual-Pixel, optics, or photography
97 |
98 | - [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)
99 | - [Fully Self-Supervised Depth Estimation from Defocus Clue](https://github.com/Ehzoahis/DEReD), CVPR 2023 | [github](https://github.com/Ehzoahis/DEReD)
100 | - [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
101 | - [Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels](https://augmentedperception.github.io/du2net/), ECCV 2022
102 | - [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)
103 | - [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)
104 | - [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)
105 |
106 |
107 |
108 | Fisheye
109 |
110 |
111 | - [SlaBins: Fisheye Depth Estimation using Slanted Bins on Road Environments](https://syniez.github.io/SlaBins/), ICCV 2023 | [github](https://github.com/Syniez/SlaBins)
112 | - [SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving](https://github.com/valeoai/WoodScape), RAL 2021 | [github](https://github.com/valeoai/WoodScape)
113 |
114 |
115 |
116 | 360 deg Depth
117 |
118 | - [FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions](https://arxiv.org/pdf/2210.01595v2), ICRA 2023 | [github](https://github.com/Sbrunoberenguel/FreDSNet)
119 | - [EGformer: Equirectangular Geometry-biased Transformer for 360 Depth Estimation](https://arxiv.org/pdf/2304.07803.pdf), ICCV 2023 | [github](https://github.com/yuniw18/EGformer)
120 | - [CRF360D: Monocular 360 Depth Estimation via Neural Spherical Fully-Connected CRFs](https://vlislab22.github.io/CRF360D/), arxiv 2024
121 | - [Distortion-Aware Self-Supervised Indoor 360∘ Depth Estimation via Hybrid Projection Fusion and Structural Regularities](https://arxiv.org/abs/2204.01027), TMM 2023
122 | - [PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation](https://arxiv.org/abs/2203.09283), ECCV 2022 | [github](https://github.com/zhijieshen-bjtu/PanoFormer)
123 | - [360MonoDepth: High-Resolution 360 ∘ Monocular Depth Estimation](https://arxiv.org/pdf/2111.15669.pdf), CVPR 2022 | [github](https://github.com/manurare/360monodepth)
124 | - [SphereDepth: Panorama Depth Estimation from Spherical Domain](https://arxiv.org/pdf/2208.13714v1.pdf), CVPR 2022 | [github](https://github.com/Yannnnnnnnnnnn/SphereDepth)
125 | - [Omnifusion: 360 monocular depth estimation via geometry-aware fusion](https://arxiv.org/abs/2203.00838), CVPR 2022 | [github](https://github.com/yuyanli0831/OmniFusion)
126 |
127 |
128 |
129 |
130 |
131 | Sparse Depth Completion
132 |
133 |
134 | - [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)
135 | - [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
136 | - [MFF-Net: Towards Efficient Monocular Depth Completion With Multi-Modal Feature Fusion](https://ieeexplore.ieee.org/abstract/document/10008014), RAL 2023
137 | - [CompletionFormer: Depth Completion with Convolutions and Vision Transformers](https://arxiv.org/pdf/2304.13030.pdf), CVPR 2023 | [github](https://github.com/youmi-zym/CompletionFormer)
138 | - [Dynamic Spatial Propagation Network for Depth Completion](https://arxiv.org/pdf/2202.09769.pdf), AAAI 2022 | [github](https://github.com/Kyakaka/DySPN)
139 | - [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)
140 | - [Monitored Distillation for Positive Congruent Depth Completion](https://arxiv.org/pdf/2203.16034.pdf), ECCV 2022 | [github](https://github.com/alexklwong/calibrated-backprojection-network)
141 | - [RigNet: Repetitive Image Guided Network for Depth Completion](https://arxiv.org/abs/2107.13802), ECCV 2022
142 | - [PENet: Towards Precise and Efficient Image Guided Depth Completion](https://arxiv.org/abs/2103.00783), ICRA 2021 | [github](https://github.com/JUGGHM/PENet_ICRA2021)
143 | - [Scene Completeness-Aware Lidar Depth Completion for Driving Scenario](https://arxiv.org/abs/2003.06945), ICASSP 2021 | [github](https://github.com/choyingw/SCADC-DepthCompletion)
144 | - [Non-Local Spatial Propagation Network for Depth Completion](https://arxiv.org/pdf/2007.10042.pdf), ECCV 2020 | [github](https://github.com/zzangjinsun/NLSPN_ECCV20)
145 | - [Depth Completion From Sparse LiDAR Data With Depth-Normal Constraints](https://arxiv.org/abs/1910.06727), ICCV 2019 | [github](https://github.com/yuyanli0831/OmniFusion)
146 | - [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)
147 | - [Deep RGB-D canonical correlation analysis for sparse depth completion](https://arxiv.org/abs/1906.08967), NeurIPS 2019 | [github](https://github.com/choyingw/CFCNet)
148 |
149 |
150 |
151 |
152 |
153 | Indoor Datasets
154 |
155 | Indoor dataset with a focus on space type
156 | - [InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation](https://arxiv.org/abs/2309.13516), BMVC 2023 | [Data Page](https://github.com/DepthComputation/InSpaceType_Benchmark)
157 | - [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)
158 |
159 |
160 | Other Domain Datasets
161 |
162 | - [SoccerNet-Depth: a Scalable Dataset for Monocular Depth Estimation in Sports Videos](https://openaccess.thecvf.com/content/CVPR2024W/CVsports/html/Leduc_SoccerNet-Depth_a_Scalable_Dataset_for_Monocular_Depth_Estimation_in_Sports_CVPRW_2024_paper.html), CVPRW 2024 | [Data Page]( https://github.com/SoccerNet/sn-depth)
163 |
164 |
165 |
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