EdgeNet: deep metric learning for 3D shapes

DOI10.1016/j.cagd.2019.04.021zbMath1432.68406OpenAlexW2944144063WikidataQ127926908 ScholiaQ127926908MaRDI QIDQ2010308

Qianfang Zou, Mingjia Chen, Changbo Wang, Li-Gang Liu

Publication date: 27 November 2019

Published in: Computer Aided Geometric Design (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cagd.2019.04.021

zbMATH Keywords

shape analysismetric learningfeature learningdeep learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05)

Uses Software

• SIFT
• SCAPE
• t-SNE
• SyncSpecCnn
• PointNet
• PointCNN
• 3DMatch
• OctNet
• VoxNet

Cites Work

• Unnamed Item
• Jointly learning shape descriptors and their correspondence via deep triplet CNNs
• Diffusion maps
• Shape distributions
• Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval
• Learning part-based templates from large collections of 3D shapes
• Discrete Geometry for Computer Imagery

This page was built for publication: EdgeNet: deep metric learning for 3D shapes