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Models/E2VID+

E2VID+

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

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

Methodology4 results

  • 3DonMVSEC
    LPIPS· 2020-03-20
    0.514
    best: 0.476 (HyperE2VID)
    SOTA
    Reducing the Sim-to-Real Gap for Event CamerasarXiv:2003.09078
  • 3DonMVSEC
    Mean Squared Error· 2020-03-20
    0.132
    best: 0.076 (HyperE2VID)
    SOTA
    Reducing the Sim-to-Real Gap for Event CamerasarXiv:2003.09078
  • 3DonEvent-Camera Dataset
    LPIPS· 2020-03-20
    0.236
    best: 0.212 (HyperE2VID)
    SOTA
    Reducing the Sim-to-Real Gap for Event CamerasarXiv:2003.09078
  • 3DonEvent-Camera Dataset
    Mean Squared Error· 2020-03-20
    0.07
    best: 0.033 (HyperE2VID)
    SOTA
    Reducing the Sim-to-Real Gap for Event CamerasarXiv:2003.09078

Computer Vision4 results

  • Video ReconstructiononMVSEC
    LPIPS· 2020-03-20
    0.514
    best: 0.476 (HyperE2VID)
    SOTA
    Reducing the Sim-to-Real Gap for Event CamerasarXiv:2003.09078
  • Video ReconstructiononMVSEC
    Mean Squared Error· 2020-03-20
    0.132
    best: 0.076 (HyperE2VID)
    SOTA
    Reducing the Sim-to-Real Gap for Event CamerasarXiv:2003.09078
  • Video ReconstructiononEvent-Camera Dataset
    LPIPS· 2020-03-20
    0.236
    best: 0.212 (HyperE2VID)
    SOTA
    Reducing the Sim-to-Real Gap for Event CamerasarXiv:2003.09078
  • Video ReconstructiononEvent-Camera Dataset
    Mean Squared Error· 2020-03-20
    0.07
    best: 0.033 (HyperE2VID)
    SOTA
    Reducing the Sim-to-Real Gap for Event CamerasarXiv:2003.09078