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Research map / 3D vision / Depth and stereo

Motion-From-Blur: 3D Shape and Motion Estimation of Motion-Blurred Objects in Videos

CVPR 2022 · 3D vision › Depth and stereo · approach: Learned optical and scene flow · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Optimize a shared 3D shape and motion across multiple blurred frames instead of reconstructing each frame independentlyMfB versus SfB · Falling ObjectsTIoU: 0.701 -> 0.772Helped
Optimize a shared 3D shape and motion across multiple blurred frames instead of reconstructing each frame independentlyMfB versus SfB · Falling ObjectsPSNR: 27.18 -> 27.54Helped
Optimize a shared 3D shape and motion across multiple blurred frames instead of reconstructing each frame independentlyMfB versus SfB · TbD-3DPSNR: 26.54 -> 26.57Helped
Optimize a shared 3D shape and motion across multiple blurred frames instead of reconstructing each frame independentlyMfB versus SfB · TbDPSNR: 25.66 -> 26.63Helped
Optimize motion across multiple framesMfB versus SfB · Synthetic 3D, rotation < 90°Translation error: 37.8 % -> 20.0 %Helped
Optimize motion across multiple framesMfB versus SfB · Synthetic 3D, rotation < 90°Rotation error: 10.9$^{\circ}$ -> 6.4$^{\circ}$Helped
Optimize motion across multiple framesMfB versus SfB · Synthetic 3D, rotation < 30°Translation error: 12.8 % -> 8.8 %Helped
Model abrupt trajectory changes with a bounceMfB versus SfB · TbD-3D bounce frames (bnc ± 0)PSNR: 20.77 -> 24.57Helped
Use multi-frame estimation around a bounceMfB versus SfB · TbD-3D frames immediately before and after bounces (bnc ± 1)PSNR: 21.77 -> 25.01Helped

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