Depth Estimation On Stanford2D3D Panoramic

Métriques

RMSE

absolute relative error

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
OmniDepth	0.6152	0.1996	OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas
SphereDepth	0.4512	0.1158	SphereDepth: Panorama Depth Estimation from Spherical Domain
Jin et al.	0.421	-	Geometric Structure Based and Regularized Depth Estimation From 360 Indoor Imagery
BiFuse with fusion	0.4142	0.1209	BiFuse: Monocular 360 Depth Estimation via Bi-Projection Fusion
HoHoNet (ResNet-101)	0.3834	0.1014	HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features
PanoDepth	0.3747	0.0972	PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation
BiFuse++	0.372	0.1117	BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation
UniFuse with fusion	0.3691	0.1114	UniFuse: Unidirectional Fusion for 360$^{\circ}$ Panorama Depth Estimation
DisConv	0.369	0.176	Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images
SliceNet	0.3684	0.0744	SliceNet: Deep Dense Depth Estimation From a Single Indoor Panorama Using a Slice-Based Representation
Neural Contourlet Network	0.3528	0.0558	Neural Contourlet Network for Monocular 360 Depth Estimation
GLPanoDepth	0.3493	-	GLPanoDepth: Global-to-Local Panoramic Depth Estimation
OmniFusion (2-iter)	0.3474	0.095	OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion
ACDNet	0.341	0.0984	ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation
PanoFormer	0.3083	0.0405	PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation
NLFB	0.2776	0.0649	Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning
FreDSNet	0.2727	0.0952	FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions
HiMODE	0.2619	0.0532	HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model

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