Monocular Depth Estimation Rankings
and 2D to 3D Video Conversion Rankings

List of Rankings

2D to 3D Video Conversion Rankings

1. Qualitative comparison of four 2D to 3D video conversion methods: Rank (human perceptual judgment)

Monocular Depth Estimation Rankings

I. Rankings based on temporal consistency metrics

1. ScanNet++ (98 video clips with 32 frames each): TAE
2. NYU-Depth V2: OPW<=0.37

II. Rankings based on 3D metrics

1. Direct comparison of 9 metric depth models (each with each) on 5 datasets: F-score

III. Rankings based on 2D metrics

1. Bonn RGB-D Dynamic (5 video clips with 110 frames each): AbsRel<=0.078
2. NYU-Depth V2: AbsRel<=0.045 (relative depth)
3. NYU-Depth V2: AbsRel (metric depth) (to do)

IV. Old layout - currently no longer up to date

1. NYU-Depth V2 (640×480): AbsRel<=0.058 (old layout - currently no longer up to date)
2. DA-2K (mostly 1500×2000): Acc (%)>=86 (old layout - currently no longer up to date)
3. UnrealStereo4K (3840×2160): AbsRel<=0.04 (old layout - currently no longer up to date)
4. Middlebury2021 (1920×1080): SqRel<=0.5 (old layout - currently no longer up to date)

Appendices

• Appendix 1: Rules for qualifying models for the rankings (to do)
• Appendix 2: Metrics selection for the rankings (to do)
• Appendix 3: List of all research papers from the above rankings

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Qualitative comparison of four 2D to 3D video conversion methods: Rank (human perceptual judgment)

📝 Note: There are no quantitative comparison results of StereoCrafter yet, so this ranking is based on my own perceptual judgement of the qualitative comparison results shown in Figure 7. One output frame (right view) is compared with one input frame (left view) from the video clip: 22_dogskateboarder and one output frame (right view) is compared with one input frame (left view) from the video clip: scooter-black

RK	Model Links:          Venue   Repository	Rank (human perceptual judgment) ↓ StereoCrafter
1	StereoCrafter	1
2-3	Immersity AI	2-3
2-3	Owl3D	2-3
4	Deep3D	4

ScanNet++ (98 video clips with 32 frames each): TAE

RK	Model Links:          Venue   Repository	TAE ↓   {Input fr.} DAV
1	Depth Any Video	2.1 {MF}
2	DepthCrafter	2.2 {MF}
3	ChronoDepth	2.3 {MF}
4	NVDS	3.7 {4}

NYU-Depth V2: OPW<=0.37

RK	Model Links:          Venue   Repository	OPW ↓   {Input fr.} FD	OPW ↓    {Input fr.} NVDS+	OPW ↓   {Input fr.} NVDS
1	FutureDepth	0.303 {4}	-	-
2	NVDS+	-	0.339 {4}	-
3	NVDS	0.364 {4}	-	0.364 {4}

Direct comparison of 9 metric depth models (each with each) on 5 datasets: F-score

📝 Note: This ranking is based on data from Table 4. The example result 3:0:2 (first left in the first row) means that Depth Pro has a better F-score than UniDepth-V in 3 datasets, in no dataset has the same F-score as UniDepth-V and has a worse F-score compared to UniDepth-V in 2 datasets.

RK	Model Links:          Venue   Repository	DP	UD	M3D v2	DA V2	DA	ZoD	M3D	PF	ZeD
1	Depth Pro	-	3:0:2	3:1:1	5:0:0	5:0:0	5:0:0	5:0:0	5:0:0	3:0:0
2	UniDepth-V	2:0:3	-	4:0:1	5:0:0	5:0:0	5:0:0	5:0:0	5:0:0	3:0:0
3	Metric3D v2 ViT-giant	1:1:3	1:0:4	-	4:1:0	5:0:0	5:0:0	5:0:0	5:0:0	3:0:0
4	Depth Anything V2	0:0:5	0:0:5	0:1:4	-	4:1:0	4:0:1	5:0:0	4:0:1	3:0:0

Bonn RGB-D Dynamic (5 video clips with 110 frames each): AbsRel<=0.078

RK	Model Links:          Venue   Repository	AbsRel ↓   {Input fr.} MonST3R	AbsRel ↓   {Input fr.} DC
1	MonST3R	0.063 {MF}	-
2	DepthCrafter	0.075 {MF}	0.075 {MF}
3	Depth Anything	-	0.078 {1}

NYU-Depth V2: AbsRel<=0.045 (relative depth)

RK	Model Links:          Venue   Repository	AbsRel ↓   {Input fr.} BD	AbsRel ↓    {Input fr.} M3D v2	AbsRel ↓   {Input fr.} DA	AbsRel ↓     {Input fr.} DA V2	-	-	-	-
1-2	BetterDepth	0.042 {1}	-	-	-	-	-	-	-
1-2	Metric3D v2 ViT-Large	-	0.042 {1}	-	-	-	-	-	-
3	Depth Anything Large	0.043 {1}	0.043 {1}	0.043 {1}	0.043 {1}	-	-	-	-
4	Depth Anything V2 Large	-	-	-	0.045 {1}	-	-	-	-

NYU-Depth V2 (640×480): AbsRel<=0.058 (old layout - currently no longer up to date)

RK	Model	AbsRel ↓   {Input fr.}	Training dataset	Official   repository	Practical model	Vapour- Synth
1-2	BetterDepth Backbone: Depth Anything & Marigold	0.042 {1}	Hypersim & Virtual KITTI	-	-	-
1-2	Metric3D v2 CSTM_label ENH: Backbone: DINOv2 with registers (ViT-L/14)	0.042 {1}	DDAD & Lyft & Driving Stereo & DIML & Arogoverse2 & Cityscapes & DSEC & Mapillary PSD & Pandaset & UASOL & Virtual KITTI & Waymo & Matterport3d & Taskonomy & Replica & ScanNet & HM3d & Hypersim		-	-
3	Depth Anything Large Backbone: DINOv2 (ViT-L/14)	0.043 {1}	Pretraining: BlendedMVS & DIML & HR-WSI & IRS & MegaDepth & TartanAir Training: BDD100K & Google Landmarks & ImageNet-21K & LSUN & Objects365 & Open Images V7 & Places365 & SA-1B		-	-
4	MiDaS v3.1 BEiTL-512 ENH: Backbone: BEiT512-L (ViT-L/16)	0.048 {1}	Pretraining: ReDWeb & HR-WSI & BlendedMVS & NYU-Depth V2 & KITTI Training: ReDWeb & DIML & 3D Movies & MegaDepth & WSVD & TartanAir & HR-WSI & ApolloScape & BlendedMVS & IRS & NYU-Depth V2 & KITTI		-
5	GeoWizard Backbone: Stable Diffusion v2	0.052 {1}	Hypersim & Replica & 3D Ken Burns & Objaverse & proprietary		-	-
6	Marigold Backbone: Stable Diffusion v2	0.055 {1}	Hypersim & Virtual KITTI		-	-
7	GenPercept Backbone: Stable Diffusion v2.1	0.056 {1}	Hypersim & Virtual KITTI		-	-
8	NeWCRFs + LightedDepth ENH:	0.057 {2}	ENH: NYU-Depth V2	ENH:	-	-
9	UniDepth-V Backbone: DINOv2 (ViT-L/14)	0.0578 {1}	A2D2 & Argoverse2 & BDD100k & CityScapes & DrivingStereo & Mapillary PSD & ScanNet & Taskonomy & Waymo		-	-

DA-2K (mostly 1500×2000): Acc (%)>=86 (old layout - currently no longer up to date)

RK	Model	Acc (%) ↑  {Input fr.}	Training dataset	Official   repository	Practical model	Vapour- Synth
1	Depth Anything V2 Giant ENH: Backbone: DINOv2 (ViT-G/14)	97.4 {1}	Pretraining: BlendedMVS & Hypersim & IRS & TartanAir & VKITTI 2 Training: BDD100K & Google Landmarks & ImageNet-21K & LSUN & Objects365 & Open Images V7 & Places365 & SA-1B	ENH:	-	-
2	GeoWizard Backbone: Stable Diffusion v2	88.1 {1}	Hypersim & Replica & 3D Ken Burns & Objaverse & proprietary		-	-
3	Marigold Backbone: Stable Diffusion v2	86.8 {1}	Hypersim & Virtual KITTI		-	-

UnrealStereo4K (3840×2160): AbsRel<=0.04 (old layout - currently no longer up to date)

RK	Model	AbsRel ↓   {Input fr.}	Training dataset	Official   repository	Practical model	Vapour- Synth
1	ZoeDepth +PFR=128 ENH:	0.0388 {1}	ENH: UnrealStereo4K	ENH:	-	-

Middlebury2021 (1920×1080): SqRel<=0.5 (old layout - currently no longer up to date)

RK	Model	SqRel ↓   {Input fr.}	Training dataset	Official   repository	Practical model	VapourSynth
1	LeReS-GBDMF ENH:	0.444 {1}	ENH: HR-WSI	ENH:	-	-

Appendix 3: List of all research papers from the above rankings

Method	Paper	Venue
BetterDepth	BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
ChronoDepth	Learning Temporally Consistent Video Depth from Video Diffusion Priors
Deep3D	Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
Depth Any Video	Depth Any Video with Scalable Synthetic Data
Depth Anything	Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Depth Anything V2	Depth Anything V2
Depth Pro	Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
DepthCrafter	DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
DPT	Vision Transformers for Dense Prediction
FutureDepth	FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
GBDMF	Multi-Resolution Monocular Depth Map Fusion by Self-Supervised Gradient-Based Composition
GenPercept	Diffusion Models Trained with Large Data Are Transferable Visual Models
GeoWizard	GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image
LeReS	Learning to Recover 3D Scene Shape from a Single Image
LightedDepth	LightedDepth: Video Depth Estimation in light of Limited Inference View Angles
Marigold	Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Metric3D	Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
Metric3D v2	Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation
MiDaS	Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer
MiDaS v3.1	MiDaS v3.1 – A Model Zoo for Robust Monocular Relative Depth Estimation
MonST3R	MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion
NeWCRFs	Neural Window Fully-connected CRFs for Monocular Depth Estimation
NVDS	Neural Video Depth Stabilizer
NVDS+	NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation
PatchFusion	PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation
StereoCrafter	StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos
UniDepth	UniDepth: Universal Monocular Metric Depth Estimation
ZoeDepth	ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth