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Research map / 3D vision / Depth and stereo

3D Video Stabilization With Depth Estimation by CNN-Based Optimization

CVPR 2021 · 3D vision › Depth and stereo · approach: Dense depth prediction and completion · 40 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add geometric loss and optical-flow loss to the reconstruction objective (ablation)Deep3D Stabilizer · NUSstability: 16.95 -> 18.29Helped
Add geometric loss to the objective that already includes photometric loss (ablation)Deep3D Stabilizer · NUSstability: 17.35 -> 18.29Helped
Add geometric loss and optical-flow loss to the reconstruction objective (ablation)Deep3D Stabilizer · NUSglobal distortion: 0.87 -> 0.92Helped
Add geometric loss to the objective that already includes photometric loss (ablation)Deep3D Stabilizer · NUSglobal distortion: 0.89 -> 0.92Helped
Add geometric loss and optical-flow loss to the reconstruction objective (ablation)Deep3D Stabilizer · NUScropping ratio: 0.57 -> 0.66Helped
Add geometric loss to the objective that already includes photometric loss (ablation)Deep3D Stabilizer · NUScropping ratio: 0.62 -> 0.66Helped
Use depth-aware 3D reprojection rather than 2D stabilization transformationsDeep3D Stabilizer · NUS large parallaxstability: 16.68 -> 16.81Helped
Apply loss-guided spatially varying depth smoothing to dynamic-object regionsDeep3D Stabilizer · videos with dynamic objectsvisual distortionHelped

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