Notice of Violation of IEEE Publication Principles: Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey

摘要

Notice of Violation of IEEE Publication Principles<br><br> "Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey,"<br> by M. M. Rahman, Y. Tan, J. Xue and K. Lu,<br> in IEEE Transactions on Image Processing, vol. 29, 2020, pp. 2947-2962<br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br> This paper contains portions of text from the paper cited below that were paraphrased without attribution.<br><br> "A Survey on 3D Object Detection Methods for Autonomous Driving Applications,"<br> by E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby and A. Mouzakitis,<br> in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, Oct. 2019, pp. 3782-3795 <br><br> <br/> With the rapid development of deep learning technology and other powerful tools, 3D object detection has made great progress and become one of the fastest growing field in computer vision. Many automated applications such as robotic navigation, autonomous driving, and virtual or augmented reality system require estimation of accurate 3D object location and detection. Under this requirement, many methods have been proposed to improve the performance of 3D object localization and detection. Despite recent efforts, 3D object detection is still a very challenging task due to occlusion, viewpoint variations, scale changes, and limited information in 3D scenes. In this paper, we present a comprehensive review of recent state-of-the-art approaches in 3D object detection technology. We start with some basic concepts, then describe some of the available datasets that are designed to facilitate the performance evaluation of 3D object detection algorithms. Next, we will review the state-of-the-art technologies in this area, highlighting their contributions, importance, and limitations as a guide for future research. Finally, we provide a quantitative comparison of the results of the state-of-the-art methods on the popular public datasets.