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Research map / 3D vision / Depth and stereo

Learning a Multi-View Stereo Machine

NeurIPS 2017 · 3D vision › Depth and stereo · approach: Cost-volume multi-view stereo · 248 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use geometrically grounded feature projection and unprojection with recurrent multi-view fusionV-LSM · ShapeNet test, 4 viewsmean voxel IoU: 62.1 -> 78.2Helped
Increase the number of input views from 1 to 4V-LSM · ShapeNet testmean voxel IoU: 61.5 -> 78.2Helped
Increase the number of input views from 1 to 43D-R2N2 w/pose · ShapeNet testmean voxel IoU: 55.1 -> 62.1Helped
Inject camera pose through fully connected layers3D-R2N2 · ShapeNet test, 4 viewsmean voxel IoU: 62.0 -> 62.1No clear effect
Train and test with random image backgrounds instead of white backgrounds (ablation)V-LSM · ShapeNet test, 1 viewmean voxel IoU: 61.5 -> 60.5Hurt
Train and test with random image backgrounds instead of white backgrounds (ablation)V-LSM · ShapeNet test, 4 viewsmean voxel IoU: 78.2 -> 75.6Hurt
Predict per-view depth maps from a globally reasoned 3D representation rather than plane-sweeping stereoDepth LSM · ShapeNet test, 10 viewsmedian absolute depth error: 0.051 -> 0.024Helped
Train with synthetically perturbed camera extrinsics (ablation)V-LSM · ShapeNet test with noisy camera posesvoxel IoUHelped
Use random image backgrounds rather than only white backgrounds (ablation)V-LSM · ShapeNet testvoxel IoUHurt

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