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SotA/Computer Vision/Surface Normals Estimation/NYU Depth v2

Surface Normals Estimation on NYU Depth v2

Metric: % < 11.25 (higher is better)

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#Model↕% < 11.25▼Extra DataPaperDate↕Code
1Metric3Dv2(L, FT)68.8YesMetric3Dv2: A Versatile Monocular Geometric Foun...2024-03-22Code
2PolyMaX(ConvNeXt-L)65.66NoPolyMaX: General Dense Prediction with Mask Tran...2023-11-09Code
3iDisc63.8NoiDisc: Internal Discretization for Monocular Dep...2023-04-13Code
4Bae et al.62.2NoEstimating and Exploiting the Aleatoric Uncertai...2021-09-20Code
5Marigold + E2E FT(zero-shot)61.4NoFine-Tuning Image-Conditional Diffusion Models i...2024-09-17Code
6Floors are Flat59.5NoFloors are Flat: Leveraging Semantics for Real-T...2019-06-16Code