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Research map / 3D vision / Depth and stereo

Learning Non-Volumetric Depth Fusion Using Successive Reprojections

CVPR 2019 · 3D vision › Depth and stereo · approach: Dense depth prediction and completion · 30 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add successive reprojection of twelve mixed close and far neighbors to COLMAP depth estimatesDeFuSR on COLMAP · DTU full point cloudcomp. (%): 72 -> 84Helped
Add successive reprojection of twelve mixed close and far neighbors to COLMAP depth estimatesDeFuSR on COLMAP · DTU per-viewacc. (%): 66 -> 89Helped
Use zero neighbors rather than neighboring-view information (ablation)DeFuSR on COLMAP · DTU full point cloudcomp. (%): 66 -> 84Hurt
Use four neighbors instead of twelve (ablation)DeFuSR on COLMAP · DTU full point cloudcomp. (%): 64 -> 84Hurt
Increase the neighbor set from four to twelve (ablation)DeFuSR on COLMAP · DTU full point cloudcomp. (%): 64 -> 84Helped
Refine MVSNet depth estimatesDeFuSR on MVSNet · DTU per-viewacc. (%): 76 -> 92Helped
Refine MVSNet depth estimatesDeFuSR on MVSNet · DTU full point cloudacc. (%): 88 -> 86Hurt
Refine MVSNet depth estimatesDeFuSR on MVSNet · DTU full point cloudcomp. (%): 66 -> 65Hurt
Use mixed close and far neighbors instead of only nearest neighbors (ablation)DeFuSR on COLMAP · DTU full point cloudcomp. (%): 72 -> 84Helped
Use mixed close and far neighbors instead of only furthest neighbors (ablation)DeFuSR on COLMAP · DTU full point cloudcomp. (%): 76 -> 84Helped

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