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FUSE introduces flow-based neural representation for 3D shape mapping

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FUSE, a novel neural representation for mapping between 3D shapes. This method utilizes flow-matching models to create an efficient and data-driven approach for cross-representation shape matching. FUSE represents 3D shapes as probability distributions derived from continuous and invertible flow mappings, enabling point mapping between surfaces by composing inverse and forward flows. The framework supports various data types including point clouds, meshes, SDFs, and volumetric data, and has demonstrated strong performance in shape matching, UV mapping, and registration tasks. AI

IMPACT Introduces a novel neural representation for 3D shape mapping, potentially improving efficiency and accuracy in computer vision tasks.

RANK_REASON The cluster contains an academic paper detailing a new method in computer vision. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FUSE introduces flow-based neural representation for 3D shape mapping

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The cluster contains an academic paper detailing a new method in computer vision. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Lorenzo Olearo, Giulio Vigan\`o, Daniele Baieri, Filippo Maggioli, Simone Melzi ·

    FUSE: A Flow-based Mapping Between Shapes

    arXiv:2511.13431v2 Announce Type: replace Abstract: We introduce a novel neural representation for maps between 3D shapes based on flow-matching models, which is computationally efficient and supports cross-representation shape matching without large-scale training or data-driven…

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