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Research map / 3D vision / Face and shape

Marr Revisited: 2D-3D Alignment via Surface Normal Prediction

CVPR 2016 · 3D vision › Face and shape · approach: Learned mesh and geometric representations · 236 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a combination of low-, mid-, and high-level VGG-16 layers rather than only low-level layers (ablation)Surface normal skip network · NYUv2 testMean angular error: 44.4 -> 19.8Helped
Add layer 7 to the five-layer set {1₂, 2₂, 3₃, 4₃, 5₃} (ablation)Surface normal skip network · NYUv2 testMean angular error: 20.9 -> 19.8Helped
Apply Manhattan-world rectification to the predicted normalsSurface normal skip network · NYUv2 test, global scene layoutMean angular error: 19.8 -> 23.9Hurt
Use Geom scoring instead of dot-product scoring for predicted-normal nearest-neighbor retrievalNearest-neighbor CAD retrieval · NYUD2 test, chairs with >50% valid depthArea under pose accuracy curve: 0.21 -> 0.31Helped
Add pool-5 appearance features to predicted-normal retrievalNearest-neighbor CAD retrieval · NYUD2 test, chairs with >50% valid depthArea under pose accuracy curve: 0.21 -> 0.25; 0.31 -> 0.29Mixed
Add pool-5 appearance features to depth-normal retrievalNearest-neighbor CAD retrieval · NYUD2 test, chairs with >50% valid depthArea under pose accuracy curve: 0.41 -> 0.36; 0.44 -> 0.38Hurt

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