Deep models for multi-view 3D object recognition: a review

被引:1
作者
Alzahrani, Mona [1 ,2 ]
Usman, Muhammad [1 ,3 ,5 ]
Jarraya, Salma Kammoun [4 ]
Anwar, Saeed [1 ,3 ]
Helmy, Tarek [1 ,5 ]
机构
[1] KFUPM, Dept Informat & Comp Sci, Dhahran, Saudi Arabia
[2] Jouf Univ, Coll Comp & Informat Sci, Sakaka, Saudi Arabia
[3] KFUPM, SDAIA KFUPM Joint Res Ctr Artificial Intelligence, Dhahran, Saudi Arabia
[4] KAU, Fac Comp & Informat Technol, Comp Sci Dept, Jeddah 21589, Saudi Arabia
[5] KFUPM, Ctr Intelligent Secure Syst, Dhahran, Saudi Arabia
关键词
3D object recognition; Multi-view object recognition; Multi-view conventional neural network; 3D object classification; 3D object retrieval; CONVOLUTIONAL NEURAL-NETWORK; CLASSIFICATION; CONTACTLESS; IMAGES;
D O I
10.1007/s10462-024-10941-w
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This review paper focuses on the progress of deep learning-based methods for multi-view 3D object recognition. It covers the state-of-the-art techniques in this field, specifically those that utilize 3D multi-view data as input representation. The paper provides a comprehensive analysis of the pipeline for deep learning-based multi-view 3D object recognition, including the various techniques employed at each stage. It also presents the latest developments in CNN-based and transformer-based models for multi-view 3D object recognition. The review discusses existing models in detail, including the datasets, camera configurations, view selection strategies, pre-trained CNN architectures, fusion strategies, and recognition performance. Additionally, it examines various computer vision applications that use multi-view classification. Finally, it highlights future directions, factors impacting recognition performance, and trends for the development of multi-view 3D object recognition method.
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页数:71
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