Multi-view convolutional vision transformer for 3D object recognition

Li, Jie; Liu, Zhao; Li, Li; Lin, Junqin; Yao, Jian; Tu, Jingmin

doi:10.1016/j.jvcir.2023.103906

Journal of Visual Communication and Image Representation

2023

DOI: 10.1016/j.jvcir.2023.103906

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Multi-view convolutional vision transformer for 3D object recognition

Jie Li,

Zhao Liu,

Li Li

et al.

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2024

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Cited by 9 publications

References 30 publications

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A systematic review of vision transformers and convolutional neural networks for Alzheimer’s disease classification using 3D MRI images

Bravo-Ortiz,

Holguin-Garcia,

Quiñones-Arredondo

et al. 2024

Neural Comput & Applic

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A systematic review of vision transformers and convolutional neural networks for Alzheimer’s disease classification using 3D MRI images

Bravo-Ortiz,

Holguin-Garcia,

Quiñones-Arredondo

et al. 2024

Neural Comput & Applic

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

Alzahrani,

Usman,

Jarraya

et al. 2024

Artif Intell Rev

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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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TNPC: Transformer-based network for point cloud classification

Zhou,

Zhao,

Xiao

et al. 2024

Expert Systems with Applications

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Multi-view convolutional vision transformer for 3D object recognition

Cited by 9 publications

References 30 publications

A systematic review of vision transformers and convolutional neural networks for Alzheimer’s disease classification using 3D MRI images

A systematic review of vision transformers and convolutional neural networks for Alzheimer’s disease classification using 3D MRI images

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

TNPC: Transformer-based network for point cloud classification

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