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Depth Estimation On Nyu Depth V2

Métriques

RMS

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	RMS	Paper Title	Repository
Freeform	0.433	Deep Optics for Monocular Depth Estimation and 3D Object Detection	-
P3Depth	0.356	P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
PAD-Net	0.792	PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing	-
EVP	0.224	EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment
Optimized, freeform	0.4325	Deep Optics for Monocular Depth Estimation and 3D Object Detection	-
Defocus/DepthNet (Normalized)	-	Focus on defocus: bridging the synthetic to real domain gap for depth estimation	-
TransDepth (AGD+ ViT)	0.365	Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction
DINOv2 (ViT-g/14 frozen, w/ DPT decoder)	0.279	DINOv2: Learning Robust Visual Features without Supervision
AdaBins	0.364	AdaBins: Depth Estimation using Adaptive Bins
VNL	0.416	Enforcing geometric constraints of virtual normal for depth prediction
SwinV2-B 1K-MIM	0.304	Revealing the Dark Secrets of Masked Image Modeling
Semantic-aware NN	0.30	3D Ken Burns Effect from a Single Image
DORN	0.509	Deep Ordinal Regression Network for Monocular Depth Estimation
SwinV2-L 1K-MIM	0.287	Revealing the Dark Secrets of Masked Image Modeling
A2J	-	A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
MS-CRF	0.586	Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
BTS	0.407	From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

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