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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023
EditorsA. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, S. Levine
PublisherNeural information processing systems foundation
ISBN (Electronic)9781713899921
Publication statusPublished - 1 Jan 2023
Event37th Conference on Neural Information Processing Systems, NeurIPS 2023 - New Orleans, United States
Duration: 10 Dec 202316 Dec 2023

Publication series

NameAdvances in Neural Information Processing Systems
Volume36
ISSN (Print)1049-5258

Conference

Conference37th Conference on Neural Information Processing Systems, NeurIPS 2023
Country/TerritoryUnited States
CityNew Orleans
Period10/12/2316/12/23

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