SGSoft introduces a template-guided pipeline that fuses semantic and geometric features to learn dense correspondences across deformable 3D shapes with claimed SOTA generalization and real-time efficiency.
arXiv preprint arXiv:2205.02904 (2022)
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MeshOn composes two input meshes realistically without intersections by using VLM-based rigid initialization, attractive geometric losses, a barrier loss, and a diffusion prior for final deformation.
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SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals
SGSoft introduces a template-guided pipeline that fuses semantic and geometric features to learn dense correspondences across deformable 3D shapes with claimed SOTA generalization and real-time efficiency.
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MeshOn: Intersection-Free Mesh-to-Mesh Composition
MeshOn composes two input meshes realistically without intersections by using VLM-based rigid initialization, attractive geometric losses, a barrier loss, and a diffusion prior for final deformation.