Depth Estimation on NYU-Depth V2

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RMS

Rank	Model	RMS	RMSE	mAP	Extra Training Data	Paper	Code	Year
1	EVP	0.224	–	–		EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment	lavreniuk/evp	2023
2	DINOv2 (ViT-g/14 frozen, w/ DPT decoder)	0.279	–	–	✓	DINOv2: Learning Robust Visual Features without Supervision	huggingface/transformers · facebookresearch/dinov2 · roboflow/rf-detr · +23	2023
3	SwinV2-L 1K-MIM	0.287	–	–		Revealing the Dark Secrets of Masked Image Modeling	SwinTransformer/MIM-Depth-Estimation	2022
4	Semantic-aware NN	0.30	–	–		3D Ken Burns Effect from a Single Image	sniklaus/3d-ken-burns · ipeter50/ken-burns-effect · agmm/colab-3d-ken-burns · +1	2019
5	SwinV2-B 1K-MIM	0.304	–	–		Revealing the Dark Secrets of Masked Image Modeling	SwinTransformer/MIM-Depth-Estimation	2022
6	P3Depth	0.356	–	–		P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior	syscv/p3depth	2022
7	AdaBins	0.364	–	–		AdaBins: Depth Estimation using Adaptive Bins	shariqfarooq123/AdaBins · martius-lab/beta-nll · dylanauty/mde-biological-vision-systems · +8	2020
8	TransDepth (AGD+ ViT)	0.365	–	–		Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction	ygjwd12345/TransDepth	2021
9	BTS	0.407	–	–		From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation	cleinc/bts · cogaplex-bts/bts · ShuweiShao/NDDepth · +11	2019
10	VNL	0.416	–	–		Enforcing geometric constraints of virtual normal for depth prediction	aim-uofa/AdelaiDepth · aim-uofa/depth · YvanYin/VNL_Monocular_Depth_Prediction	2019
11	Optimized, freeform	0.4325	–	–		Deep Optics for Monocular Depth Estimation and 3D Object Detection		2019
12	Freeform	0.433	–	–		Deep Optics for Monocular Depth Estimation and 3D Object Detection		2019
13	DORN	0.509	–	–		Deep Ordinal Regression Network for Monocular Depth Estimation	hufu6371/DORN · dontLoveBugs/DORN_pytorch · sjsu-smart-lab/Self-supervised-Monocular-Trained-Depth-Estimation-using-Self-attention-and-Discrete-Disparity-Volum · +2	2018
14	MS-CRF	0.586	–	–		Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation	danxuhk/ContinuousCRF-CNN · xuyingyue/DeepUnifiedCRF_iccv19	2017
15	PAD-Net	0.792	–	–		PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing		2018
16	Defocus/DepthNet (Normalized)	–	0.013	–		Focus on defocus: bridging the synthetic to real domain gap for depth estimation	dvl-tum/defocus-net	2020
17	A2J	–	–	8.61		A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image	zhangboshen/A2J · bo-zhang-cs/CACNet-Pytorch	2019
18	EVP	0.224	–	–		EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment	lavreniuk/evp	2023
19	DINOv2 (ViT-g/14 frozen, w/ DPT decoder)	0.279	–	–	✓	DINOv2: Learning Robust Visual Features without Supervision	huggingface/transformers · facebookresearch/dinov2 · roboflow/rf-detr · +23	2023
20	SwinV2-L 1K-MIM	0.287	–	–		Revealing the Dark Secrets of Masked Image Modeling	SwinTransformer/MIM-Depth-Estimation	2022