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Research map / Probabilistic and scientific modeling / Molecules and proteins

Full Flow: Optical Flow Estimation By Global Optimization Over Regular Grids

CVPR 2016 · Probabilistic and scientific modeling › Molecules and proteins · approach: Flow matching generative modeling · 165 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use patch-based NCC instead of the pixelwise Horn-Schunck data term (ablation)FullFlow global optimization · MPI Sintel training, final pass, 5% subsetEPE: 5.972 -> 2.710Helped
Use patch-based NCC instead of the pixelwise Horn-Schunck data term (ablation)FullFlow global optimization · MPI Sintel training, final pass, 5% subsetEPE: 6.337 -> 2.896Helped
Use L1 rather than Charbonnier or squared L2 regularization with NCC (ablation)FullFlow global optimization · MPI Sintel training, final pass, 5% subsetEPE: 2.883 -> 2.710; 2.896 -> 2.710Helped
Remove truncation from NCC + L1 regularization (ablation)FullFlow global optimization · MPI Sintel training, final pass, 5% subsetEPE: 2.710 -> 2.710No clear effect
Remove truncation from NCC + L2 regularization (ablation)FullFlow global optimization · MPI Sintel training, final pass, 5% subsetEPE: 2.896 -> 2.976Hurt
Use FullFlow instead of FlowFieldsMPI Sintel test · final pass, allEPE: 5.810 -> 5.895Hurt
Use FullFlow instead of DiscreteFlowMPI Sintel test · clean pass, allEPE: 3.567 -> 3.601Hurt
Use FullFlow instead of DiscreteFlowKITTI 2015 test · alloutlier percentage: 22.38% -> 24.26%Hurt

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