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Research map / 3D vision / Depth and stereo

RayMVSNet: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo

CVPR 2022 · 3D vision › Depth and stereo · approach: Cost-volume multi-view stereo · 160 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add the epipolar transformer (ablation)RayMVSNet · DTU testOverall distance (mm): 0.343 -> 0.330Helped
add 2D image features (ablation)RayMVSNet · DTU testOverall distance (mm): 0.348 -> 0.330Helped
add 3D volume features (ablation)RayMVSNet · DTU testOverall distance (mm): 0.378 -> 0.330Helped
use epipolar transformer instead of visibility-aware max-pooling aggregation (ablation)RayMVSNet · DTU testOverall distance (mm): 0.338 -> 0.330Helped
use ray-based local fields instead of one global implicit field (ablation)RayMVSNet · DTU testOverall distance (mm): 0.608 -> 0.330Helped
use LSTM instead of Transformer for sequential ray modeling (ablation)RayMVSNet · DTU testOverall distance (mm): 0.341 -> 0.330Helped
use LSTM instead of average pooling for ray aggregation (ablation)RayMVSNet · DTU testOverall distance (mm): 0.381 -> 0.330Helped
use LSTM instead of max pooling for ray aggregation (ablation)RayMVSNet · DTU testOverall distance (mm): 0.424 -> 0.330Helped
add auxiliary SDF prediction (ablation)RayMVSNet · DTU testOverall distance (mm): 0.342 -> 0.330Helped
add moderate Gaussian perturbation to coarse depth during training and testingRayMVSNet · DTU testdepth prediction qualityHelped

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