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Neural Template: Topology-Aware Reconstruction and Disentangled Generation of 3D Meshes

CVPR 2022 · 3D vision › Face and shape · approach: Textured mesh generation · 26 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DT-Net's input-specific topology-aware template and topology-preserving deformation instead of IM-NETShapeNet voxel reconstruction · ShapeNet voxels, meanLFD: 2918.9 -> 2835.0Helped
Use DT-Net's input-specific topology-aware template and topology-preserving deformation instead of IM-NETShapeNet voxel reconstruction · ShapeNet voxels, meanP2F: 0.820 -> 0.796Helped
Use DT-Net's input-specific topology-aware template and topology-preserving deformation instead of IM-NETShapeNet voxel reconstruction · ShapeNet voxels, meanCD: 0.648 -> 0.573Helped
Use DT-Net instead of TMNetShapeNet single-view image reconstruction · ShapeNet imagesLFD: 3765.5 -> 3388.3Helped
Use DT-Net instead of TMNetShapeNet single-view image reconstruction · ShapeNet imagesP2F: 1.285 -> 1.294Hurt
Use DT-Net instead of AtlasNetShapeNet single-view image reconstruction · ShapeNet imagesCD: 1.041 -> 1.396Hurt
Replace convex primitives with superquadrics for topology-template composition (ablation)DT-Net · 3D mesh reconstructionreconstruction qualityHurt
Replace convex primitives with cuboids for topology-template composition (ablation)DT-Net · 3D mesh reconstructionreconstruction qualityHurt
Replace the NODE deformation module with an invertible neural network (ablation)DT-Net · 3D mesh reconstructionreconstruction qualityHurt

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