PRISM is a pre-training method that learns isometric latent embeddings by explicitly recovering surface geodesic distances with a topology-enforcing loss and a two-stage training schedule.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
CSCD generalizes LS to continuous domain with CSCD-M using intrinsic triangulation for meshes and CSCD-PC using tufted Laplacians for point clouds, claiming to match or outperform priors on benchmarks.
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From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data
PRISM is a pre-training method that learns isometric latent embeddings by explicitly recovering surface geodesic distances with a topology-enforcing loss and a two-stage training schedule.
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Curve Skeletonization in Continuous domain for Meshes and Point Clouds
CSCD generalizes LS to continuous domain with CSCD-M using intrinsic triangulation for meshes and CSCD-PC using tufted Laplacians for point clouds, claiming to match or outperform priors on benchmarks.