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Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction

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Résumé

We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data.SNK operates on a single pair of shapes, and employs a reconstruction-based strategy using an encoder-decoder architecture, which deforms the source shape to closely match the target shape.During the process, an unsupervised functional map is predicted and converted into a point-to-point map, serving as a supervisory mechanism for the reconstruction.To aid in training, we have designed a new decoder architecture that generates smooth, realistic deformations.SNK demonstrates competitive results on traditional benchmarks, simplifying the shape-matching process without compromising accuracy.Our code can be found online: https://github.com/pvnieo/SNK.

langue originaleAnglais
titreAdvances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023
rédacteurs en chefA. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, S. Levine
EditeurNeural information processing systems foundation
ISBN (Electronique)9781713899921
étatPublié - 1 janv. 2023
Evénement37th Conference on Neural Information Processing Systems, NeurIPS 2023 - New Orleans, États-Unis
Durée: 10 déc. 202316 déc. 2023

Série de publications

NomAdvances in Neural Information Processing Systems
Volume36
ISSN (imprimé)1049-5258

Une conférence

Une conférence37th Conference on Neural Information Processing Systems, NeurIPS 2023
Pays/TerritoireÉtats-Unis
La villeNew Orleans
période10/12/2316/12/23

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