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03/29/2023
Understanding and Improving Features Learned in Deep Functional Maps
Deep functional maps have recently emerged as a successful paradigm for ...
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03/27/2023
Generalizable Local Feature Pre-training for Deformable Shape Analysis
Transfer learning is fundamental for addressing problems in settings wit...
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01/14/2023
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching
We present Neural Correspondence Prior (NCP), a new paradigm for computi...
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09/16/2022
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence
In this work, we present a novel learning-based framework that combines ...
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12/14/2021
Why you should learn functional basis
Efficient and practical representation of geometric data is a ubiquitous...
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10/19/2021
DPFM: Deep Partial Functional Maps
We consider the problem of computing dense correspondences between non-r...
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12/01/2020
