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Research map / 3D vision / Depth and stereo

Generalized Binary Search Network for Highly-Efficient Multi-View Stereo

CVPR 2022 · 3D vision › Depth and stereo · approach: Cost-volume multi-view stereo · 93 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace dense depth regression with binary-search classification (ablation)MVS depth estimation · DTUOverall: 0.355 -> 0.360Helped
Add error tolerance bins to binary search (ablation)GBi-Net · DTUOverall: 0.360 -> 0.298Helped
Increase from 1 to 2 error tolerance bins on both sides (ablation)GBi-Net · DTUOverall: 0.298 -> 0.300Hurt
Increase from 1 to 2 error tolerance bins on both sides (ablation)GBi-Net · DTU<0.125 depth accuracy: 11.75 -> 13.58Helped
Increase from 1 to 2 error tolerance bins on both sides (ablation)GBi-Net · DTUMemory (MB): 2108 -> 2438Hurt
Use efficient gradient updating rather than accumulating gradients across stages (ablation)GBi-Net · DTUTraining memory (MB): 12137 -> 5208Helped
Use efficient gradient updating rather than accumulating gradients across stages (ablation)GBi-Net · DTUOverall: 0.299 -> 0.298No clear effect
Remove random cropping data augmentation (ablation)GBi-Net · DTUOverall: 0.289 -> 0.298Hurt
Increase the number of search stages from 6 toward 8 (ablation)GBi-Net · DTUReconstruction qualityHelped
Use GBi-Net instead of Bi-Net, adding error tolerance bins and the generalized search mechanisms (ablation)MVS depth estimation · Tanks and Temples AdvancedMean: 32.03 -> 37.32Helped

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