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Models/DORN

DORN

Reported on 16 benchmarks across 2 tasks · 1 paper · 14 SOTA

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

Computer Vision8 results

  • Depth EstimationonNYU-Depth V2
    RMSE· 2018-06-06
    0.509
    best: 0.013 (Defocus/DepthNet (Normalized))
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • Depth EstimationonKITTI Eigen split
    Delta < 1.25· 2018-06-06
    0.932
    best: 0.99 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • Depth EstimationonKITTI Eigen split
    Delta < 1.25^2· 2018-06-06
    0.984
    best: 0.999 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • Depth EstimationonKITTI Eigen split
    Delta < 1.25^3· 2018-06-06
    0.994
    best: 1 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • Depth EstimationonKITTI Eigen split
    RMSE· 2018-06-06
    2.727
    best: 1.394 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • Depth EstimationonKITTI Eigen split
    RMSE log· 2018-06-06
    0.12
    best: 0.048 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • Depth EstimationonKITTI Eigen split
    absolute relative error· 2018-06-06
    0.072
    best: 0.029 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • Depth EstimationonNYU-Depth V2
    RMS· 2018-06-06
    0.509
    best: 0.792 (PAD-Net)
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446

Methodology8 results

  • 3DonNYU-Depth V2
    RMSE· 2018-06-06
    0.509
    best: 0.013 (Defocus/DepthNet (Normalized))
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • 3DonKITTI Eigen split
    Delta < 1.25· 2018-06-06
    0.932
    best: 0.99 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • 3DonKITTI Eigen split
    Delta < 1.25^2· 2018-06-06
    0.984
    best: 0.999 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • 3DonKITTI Eigen split
    Delta < 1.25^3· 2018-06-06
    0.994
    best: 1 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • 3DonKITTI Eigen split
    RMSE· 2018-06-06
    2.727
    best: 1.394 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • 3DonKITTI Eigen split
    RMSE log· 2018-06-06
    0.12
    best: 0.048 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • 3DonKITTI Eigen split
    absolute relative error· 2018-06-06
    0.072
    best: 0.029 (SPIDepth)
    SOTA
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446
  • 3DonNYU-Depth V2
    RMS· 2018-06-06
    0.509
    best: 0.792 (PAD-Net)
    Deep Ordinal Regression Network for Monocular Depth EstimationarXiv:1806.02446