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SotA/Computer Vision/Depth Estimation/NYU-Depth V2 self-supervised

Depth Estimation on NYU-Depth V2 self-supervised

Metric: Absolute relative error (AbsRel) (lower is better)

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#Model↕Absolute relative error (AbsRel)▲Extra DataPaperDate↕Code
1IndoorDepth0.126NoDeeper into Self-Supervised Monocular Indoor Dep...2023-12-03Code
2DistDepth0.13NoToward Practical Monocular Indoor Depth Estimation2021-12-04Code
3MonoIndoor0.134NoMonoIndoor: Towards Good Practice of Self-Superv...2021-07-26-
4StrutDepth0.142NoStructDepth: Leveraging the structural regularit...2021-08-19Code
5P2Net+PP0.147NoP$^{2}$Net: Patch-match and Plane-regularization...2020-07-15Code
6Bian et al0.157NoUnsupervised Scale-consistent Depth Learning fro...2021-05-25Code
7Zhao et al0.189NoTowards Better Generalization: Joint Depth-Pose ...2020-04-03Code
8Zhou et al0.208NoMoving Indoor: Unsupervised Video Depth Learning...2019-10-20-