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Models/UCSNet

UCSNet

Reported on 7 benchmarks across 3 tasks · 1 paper

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

Methodology6 results

  • 3D ReconstructiononDTU
    Acc· 2019-11-27
    0.338
    best: 0.427 (PatchmatchNet)
    Deep Stereo using Adaptive Thin Volume Representation with Uncertainty AwarenessarXiv:1911.12012
  • 3D ReconstructiononDTU
    Comp· 2019-11-27
    0.349
    best: 0.884 (3D-R2N2)
    Deep Stereo using Adaptive Thin Volume Representation with Uncertainty AwarenessarXiv:1911.12012
  • 3D ReconstructiononDTU
    Overall· 2019-11-27
    0.344
    best: 0.63 (3D-R2N2)
    Deep Stereo using Adaptive Thin Volume Representation with Uncertainty AwarenessarXiv:1911.12012
  • 3DonDTU
    Acc· 2019-11-27
    0.338
    best: 0.427 (PatchmatchNet)
    Deep Stereo using Adaptive Thin Volume Representation with Uncertainty AwarenessarXiv:1911.12012
  • 3DonDTU
    Comp· 2019-11-27
    0.349
    best: 0.884 (3D-R2N2)
    Deep Stereo using Adaptive Thin Volume Representation with Uncertainty AwarenessarXiv:1911.12012
  • 3DonDTU
    Overall· 2019-11-27
    0.344
    best: 0.63 (3D-R2N2)
    Deep Stereo using Adaptive Thin Volume Representation with Uncertainty AwarenessarXiv:1911.12012

Computer Vision1 result

  • Point CloudsonTanks and Temples
    Mean F1 (Intermediate)· 2019-11-27
    54.83
    best: 67.03 (MVSFormer++)
    Deep Stereo using Adaptive Thin Volume Representation with Uncertainty AwarenessarXiv:1911.12012