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Research map / 3D vision / Depth and stereo

PatchmatchNet: Learned Multi-View Patchmatch Stereo

CVPR 2021 · 3D vision › Depth and stereo · approach: Cost-volume multi-view stereo · 424 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add adaptive propagation (ablation)PatchmatchNet · DTU evaluation setOverall (mm): 0.361 -> 0.352Helped
add adaptive spatial cost aggregation (ablation)PatchmatchNet · DTU evaluation setOverall (mm): 0.380 -> 0.352Helped
add both adaptive propagation and adaptive evaluation (ablation)PatchmatchNet · DTU evaluation setOverall (mm): 0.396 -> 0.352Helped
increase iterations from 1,1,1 to 2,2,1 (ablation)PatchmatchNet · DTU evaluation setOverall (mm): 0.362 -> 0.352Helped
increase iterations from 2,2,1 to 3,3,1 (ablation)PatchmatchNet · DTU evaluation setOverall (mm): 0.352 -> 0.351Helped
increase iterations beyond 3,3,1 to 4,4,1 or 5,5,1 (ablation)PatchmatchNet · DTU evaluation setOverall (mm): 0.351 -> 0.351No clear effect
add pixel-wise view weighting (ablation)PatchmatchNet · Tanks & Temples IntermediateF score: 52.46 -> 53.15Helped
add pixel-wise view weighting (ablation)PatchmatchNet · ETH3D TrainingF1 score: 61.32 -> 64.21Helped
use robust training with random selection from the ten best source views (ablation)PatchmatchNet · Tanks & Temples IntermediateF score: 52.12 -> 53.15Helped
use robust training with random selection from the ten best source views (ablation)PatchmatchNet · ETH3D TrainingF1 score: 62.57 -> 64.21Helped
increase input views from 2 to 5 (ablation)PatchmatchNet · DTU evaluation setOverall (mm): 0.385 -> 0.352Helped
increase input views from 5 to 6 (ablation)PatchmatchNet · DTU evaluation setOverall (mm): 0.352 -> 0.353Hurt

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