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Models/BinsFormer

BinsFormer

Reported on 26 benchmarks across 2 tasks · 1 paper · 4 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision13 results

  • Depth EstimationonNYU-Depth V2
    Delta < 1.25· 2022-04-03
    0.925
    best: 0.989 (UniK3D (FT, metric))
    SOTA
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonKITTI Eigen split
    RMSE· uses extra data· 2022-04-03
    2.098
    best: 1.394 (SPIDepth)
    SOTA
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonNYU-Depth V2
    Delta < 1.25^2· 2022-04-03
    0.989
    best: 1 (HybridDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonNYU-Depth V2
    Delta < 1.25^3· 2022-04-03
    0.997
    best: 1 (HybridDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonNYU-Depth V2
    RMSE· 2022-04-03
    0.33
    best: 0.013 (Defocus/DepthNet (Normalized))
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonNYU-Depth V2
    absolute relative error· 2022-04-03
    0.094
    best: 0.026 (HybridDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonNYU-Depth V2
    log 10· 2022-04-03
    0.04
    best: 0.059 (SC-DepthV2)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonKITTI Eigen split
    Delta < 1.25· uses extra data· 2022-04-03
    0.974
    best: 0.99 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonKITTI Eigen split
    Delta < 1.25^2· uses extra data· 2022-04-03
    0.997
    best: 0.999 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonKITTI Eigen split
    Delta < 1.25^3· uses extra data· 2022-04-03
    0.999
    best: 1 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonKITTI Eigen split
    RMSE log· uses extra data· 2022-04-03
    0.079
    best: 0.048 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonKITTI Eigen split
    Sq Rel· uses extra data· 2022-04-03
    0.151
    best: 0.224 (SfM-Revisited)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • Depth EstimationonKITTI Eigen split
    absolute relative error· uses extra data· 2022-04-03
    0.052
    best: 0.029 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987

Methodology13 results

  • 3DonNYU-Depth V2
    Delta < 1.25· 2022-04-03
    0.925
    best: 0.989 (UniK3D (FT, metric))
    SOTA
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonKITTI Eigen split
    RMSE· uses extra data· 2022-04-03
    2.098
    best: 1.394 (SPIDepth)
    SOTA
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonNYU-Depth V2
    Delta < 1.25^2· 2022-04-03
    0.989
    best: 1 (HybridDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonNYU-Depth V2
    Delta < 1.25^3· 2022-04-03
    0.997
    best: 1 (HybridDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonNYU-Depth V2
    RMSE· 2022-04-03
    0.33
    best: 0.013 (Defocus/DepthNet (Normalized))
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonNYU-Depth V2
    absolute relative error· 2022-04-03
    0.094
    best: 0.026 (HybridDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonNYU-Depth V2
    log 10· 2022-04-03
    0.04
    best: 0.059 (SC-DepthV2)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonKITTI Eigen split
    Delta < 1.25· uses extra data· 2022-04-03
    0.974
    best: 0.99 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonKITTI Eigen split
    Delta < 1.25^2· uses extra data· 2022-04-03
    0.997
    best: 0.999 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonKITTI Eigen split
    Delta < 1.25^3· uses extra data· 2022-04-03
    0.999
    best: 1 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonKITTI Eigen split
    RMSE log· uses extra data· 2022-04-03
    0.079
    best: 0.048 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonKITTI Eigen split
    Sq Rel· uses extra data· 2022-04-03
    0.151
    best: 0.224 (SfM-Revisited)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987
  • 3DonKITTI Eigen split
    absolute relative error· uses extra data· 2022-04-03
    0.052
    best: 0.029 (SPIDepth)
    BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationarXiv:2204.00987