Surface Normals Estimation on NYU Depth v2

Metric: % < 22.5 (higher is better)

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#	Model↕	% < 22.5▼	Extra Data	Paper	Date↕	Code
1	Metric3Dv2(L, FT)	84.9	Yes	Metric3Dv2: A Versatile Monocular Geometric Foun...	2024-03-22	Code
2	PolyMaX(ConvNeXt-L)	82.28	No	PolyMaX: General Dense Prediction with Mask Tran...	2023-11-09	Code
3	iDisc	79.8	No	iDisc: Internal Discretization for Monocular Dep...	2023-04-13	Code
4	Bae et al.	79.3	No	Estimating and Exploiting the Aleatoric Uncertai...	2021-09-20	Code
5	Floors are Flat	72.2	No	Floors are Flat: Leveraging Semantics for Real-T...	2019-06-16	Code

#1Metric3Dv2(L, FT)SOTA
84.9
% < 22.5· Extra Data· 2024-03-22
Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation Code
#2PolyMaX(ConvNeXt-L)SOTA
82.28
% < 22.5· 2023-11-09
PolyMaX: General Dense Prediction with Mask Transformer Code
#3iDiscSOTA
79.8
% < 22.5· 2023-04-13
iDisc: Internal Discretization for Monocular Depth Estimation Code
#4Bae et al.SOTA
79.3
% < 22.5· 2021-09-20
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation Code
#5Floors are FlatSOTA
72.2
% < 22.5· 2019-06-16
Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction Code