Deep point-to-subspace metric learning for sketch-based 3D shape retrieval-Reference-Cited by-同舟云学术

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Deep point-to-subspace metric learning for sketch-based 3D shape retrieval

Published:2019-12 Issue: Volume:96 Page:106981
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Lei Yinjie^ORCID,Zhou Ziqin,Zhang Pingping^ORCID,Guo Yulan^ORCID,Ma Zijun,Liu Lingqiao

Funder

National Natural Science Foundation of China

ARC

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference53 articles.

1. The princeton shape benchmark;Shilane,2004

2. A new 3D model retrieval approach based on the elevation descriptor;Shih;Pattern Recognit.,2007

3. Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval;Pickup;Pattern Recognit.,2015

4. Monocular 3D facial shape reconstruction from a single 2D image with coupled-dictionary learning and sparse coding;Dou;Pattern Recognit.,2018

5. Recognition of feature curves on 3D shapes using an algebraic approach to hough transforms;Torrente;Pattern Recognit.,2018

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2. Global semantics correlation transmitting and learning for sketch-based cross-domain visual retrieval;Complex & Intelligent Systems;2024-06-29

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4. Sketch-Based 3D Shape Retrieval With Multi-View Fusion Transformer;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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