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Depth Estimation On Nyu Depth V2

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RMS

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	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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