Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations-Reference-Cited by-同舟云学术

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Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations

Published:2023-12 Issue:12 Volume:45 Page:14872-14887
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

He Pan¹^ORCID,Emami Patrick²^ORCID,Ranka Sanjay³^ORCID,Rangarajan Anand³^ORCID

Affiliation:

1. Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA

2. National Renewable Energy Lab, Golden, CO, USA

3. Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA

Funder

NSF CNS

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

https://ieeexplore.ieee.org/ielam/34/10308548/10239471-aam.pdf

Reference92 articles.

1. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

2. Feature-based similarity search in 3D object databases

3. Learning shape correspondence with anisotropic convolutional neural networks;boscaini;Proc Adv Neural Inf Process Syst,2016

4. Reduced representation of deformation fields for effective non-rigid shape matching;sundararaman;Proc Adv Neural Inf Process Syst,2022

5. The Average Mixing Kernel Signature

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