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Research map / Point clouds / Shape and surface completion

Cross-modal Learning for Image-Guided Point Cloud Shape Completion

NeurIPS 2022 · Point clouds › Shape and surface completion · approach: Point cloud completion networks · 19 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add the auxiliary image via cross-attention fusion (multimodal vs unimodal) (ablation)XMFnet · ShapeNet-ViPC avg of 3 categoriesChamfer Distance x1e-3: 1.570 -> 1.470Helped
use best vs worst viewpoint image (ablation)XMFnet · ShapeNet-ViPC avg of 3 categoriesChamfer Distance x1e-3: best 1.223, worst 1.819 vs avg 1.470Mixed
add mixup to resampling for weak supervision (ablation)XMFnet weakly-sup. · ShapeNet-ViPC AirplaneChamfer Distance x1e-3: 4.568 -> 4.239Helped
add differentiable silhouette rendering loss (ablation)XMFnet weakly-sup. · ShapeNet-ViPC AirplaneChamfer Distance x1e-3: 4.239 -> 2.426Helped
add density-aware Chamfer Distance to weak-supervision loss (ablation)XMFnet weakly-sup. · ShapeNet-ViPC CabinetChamfer Distance x1e-3: 3.012 -> 2.426Helped
latent cross-attention fusion instead of coarse point cloud from single-view reconstructionXMFnet vs ViPC · ShapeNet-ViPC avgChamfer Distance x1e-3: 3.308 -> 1.443Helped
weakly-supervised image rendering supervision vs unimodal self-supervised VRC-NetXMFnet · ShapeNet-ViPC Airplane/Lamp/WatercraftChamfer Distance x1e-3: 4.315 -> 2.426 (Airplane)Helped

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