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

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Title: Deep models for multi-view 3D object recognition: a review.
Authors: Alzahrani, Mona, Usman, Muhammad, Jarraya, Salma Kammoun, Anwar, Saeed, Helmy, Tarek
Source: Artificial Intelligence Review; Dec2024, Vol. 57 Issue 12, p1-71, 71p
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:02692821
DOI:10.1007/s10462-024-10941-w