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Determining Occlusions From Space and Time Image Reconstructions

CVPR 2016 · Video › Video segmentation and tracking · approach: Learned optical flow estimation · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use reconstruction-based occlusion modeling with the full energy-minimization framework instead of the learning-based detectorOcclusion detection on MPI Sintel · MPI Sintel, 69 sequences (Oracle 69)F-score: 0.535 -> 0.550Helped
Use reconstruction-based occlusion modeling with the full energy-minimization framework instead of the strongest baselineOcclusion detection on MPI Sintel · MPI Sintel, 69 sequences (Global 69)F-score: 0.449 -> 0.540Helped
Use reconstruction-based occlusion modeling with the full energy-minimization framework instead of the strongest reported baselineOcclusion detection on MPI Sintel · MPI Sintel Final rendering passF-score: 0.398 -> 0.491Helped
Replace displaced-frame-difference detection with the reconstruction-based local color-model criterionPixel-wise occlusion detection · Eight MPI Sintel sequencesAUCHelped
Use reconstruction-based detection with estimated or noisy flow rather than relying on accurate ground-truth flowPixel-wise occlusion detection · MPI Sintel sequencesAUCHelped
Use two-component GMM color models in textureless regionsFull occlusion detection method · MPI Sintelfalse-positive rateHurt

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