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Abstract

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of freedom) pose assumptions are widely discussed and studied in the field. In the 3D object detection process, classifications are centered on the object's size, position, and direction. And in 6D pose assumptions, networks emphasize 3D translation and rotation vectors. Successful application of these strategies can have a huge impact on various machine learning-based applications, including the autonomous vehicles, the robotics industry, and the augmented reality sector. Although extensive work has been done on 3D object detection with a pose assumption from RGB images, the challenges have not been fully resolved. Our analysis provides a comprehensive review of the proposed contemporary techniques for complete 3D object detection and the recovery of 6D pose assumptions of an object. In this review research paper, we have discussed several proposed sophisticated methods in 3D object detection and 6D pose estimation, including some popular data sets, evaluation matrix, and proposed method challenges. Most importantly, this study makes an effort to offer some possible future directions in 3D object detection and 6D pose estimation. We accept the autonomous vehicle as the sample case for this detailed review. Finally, this review provides a complete overview of the latest in-depth learning-based research studies related to 3D object detection and 6D pose estimation systems and points out a comparison between some popular frameworks. To be more concise, we propose a detailed summary of the state-of-the-art techniques of modern deep learning-based object detection and pose estimation models.

Item Type:	Article
Authors/Creators:	Hoque, S and Arafat, M and Xu, S and Maiti, A and Wei, Y
Keywords:	machine learning, deep neural network, computer vision, image processing, convolutional neural network, 3D object detection, 6D pose estimation
Journal or Publication Title:	IEEE Access
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2169-3536
DOI / ID Number:	https://doi.org/10.1109/ACCESS.2021.3114399
Copyright Information:	Copyright 2021 The Author(s)Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
Related URLs:	Google Scholar: Xu, S UTAS Profile: Xu, S
Item Statistics:	View statistics for this item

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