RFMNet: Robust Deep Functional Maps for unsupervised non-rigid shape correspondence
Open Access
- 29 July 2023
- journal article
- research article
- Published by Elsevier in Graphical Models
- Vol. 129, 101189
Abstract
No abstract availableKeywords
This publication has 22 references indexed in Scilit:
- Deep Functional Maps: Structured Prediction for Dense Shape Correspondence2017
- Geometric Deep Learning: Going beyond Euclidean dataIEEE Signal Processing Magazine, 2017
- Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs2017
- Informative Descriptor Preservation via Commutativity for Shape MatchingComputer Graphics Forum, 2017
- Partial Functional CorrespondenceComputer Graphics Forum, 2016
- Geodesic Convolutional Neural Networks on Riemannian Manifolds2015
- SHOT: Unique signatures of histograms for surface and texture descriptionComputer Vision and Image Understanding, 2014
- A Survey on Shape CorrespondenceComputer Graphics Forum, 2011
- A Concise and Provably Informative Multi‐Scale Signature Based on Heat DiffusionComputer Graphics Forum, 2009
- SCAPEACM Transactions on Graphics, 2005