3D model classification via Principal Thickness Images-Reference-Cited by-同舟云学术

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3D model classification via Principal Thickness Images

Published:2016-09 Issue: Volume:78 Page:199-208
ISSN:0010-4485
Container-title:Computer-Aided Design
language:en
Short-container-title:Computer-Aided Design

Author:

Shu Zhenyu,Xin Shiqing,Xu Huixia,Kavan Ladislav,Wang Pengfei,Liu Ligang

Funder

Natural Science Foundation of Ningbo

National Natural Science Foundation of China

Chinese Academy of Sciences

Natural Science Foundation of Zhejiang Province

Opening Project of Zhejiang Provincial Top Key Discipline of Pharmaceutical Sciences

National Science Foundation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference43 articles.

1. Su H, Maji S, Kalogerakis E, Learned-Miller E. Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE international conference on computer vision, 2015, pp. 945–53.

2. Learning view-model joint relevance for 3D object retrieval;Ke;IEEE Trans Image Process,2015

3. Kernel sparse representation-based classifier;Zhang;IEEE Trans Signal Process,2012

4. Shape distributions;Osada;ACM Trans Graph,2002

5. Rotation invariant spherical harmonic representation of 3D shape descriptors;Kazhdan,2003

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1. Lightweight Multi-View-Group Neural Network for 3D Shape Classification;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08

2. Multi-Modal Meta-Transfer Fusion Network for Few-Shot 3D Model Classification;International Journal of Computer Vision;2023-10-05

3. Feature-preserved convolutional neural network for 3D mesh recognition;Applied Soft Computing;2022-10

4. 3D Shape Segmentation Using Soft Density Peak Clustering and Semi-Supervised Learning;Computer-Aided Design;2022-04

5. Object Synthesis by Learning Part Geometry with Surface and Volumetric Representations;Computer-Aided Design;2021-01

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