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Research map / Point clouds / Shape and surface completion

POCO: Point Convolution for Surface Reconstruction

CVPR 2022 · Point clouds › Shape and surface completion · approach: Distance-function surface reconstruction · 146 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use point convolutions with FKAConv instead of Residual PointNet (ablation)POCO · ShapeNet chairs train, 13-class generalizationIoU: 0.661 -> 0.882Helped
Increase interpolation neighbors from 1 to 64 (ablation)POCO · ShapeNet chairs train, 13-class generalizationIoU: 0.799 -> 0.882Helped
Increase interpolation neighbors from 64 to 128 (ablation)POCO · ShapeNet chairs train, 13-class generalizationCD: 4.069 -> 3.611Helped
Use multi-head rather than single-head attention (ablation)POCO · ShapeNet chairs train, 13-class generalizationIoU: 0.879 -> 0.895Helped
Interpolate relative rather than global features (ablation)POCO · ShapeNet chairs train, 13-class generalizationIoU: 0.854 -> 0.882Helped
Use learned multi-head attention rather than mean interpolation of relative features (ablation)POCO · ShapeNet chairs train, 13-class generalizationIoU: 0.883 -> 0.895Helped
Apply latent-level test-time augmentation with 10 views (ablation)POCO · ABC, Famous, and Thingi10kCD: 1.87 -> 1.77; 1.56 -> 1.40; 1.47 -> 1.39Helped
Use POCO instead of DP-ConvONetShapeNet · ShapeNetIoU: 0.895 -> 0.926Helped
Use POCO instead of ConvONetSynthetic Rooms · Synthetic RoomsIoU: 0.849 -> 0.884Helped
Use POCO instead of LIGSceneNet reconstruction · SceneNet, 20 pts/m²CD: 1.52 -> 0.84Helped
Use POCO instead of LIGSceneNet reconstruction · SceneNet, 100 pts/m²CD: 0.97 -> 0.57Helped

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