3D Object Detection Based on Convolutional Neural Networks: A Survey
WANG Yadong1, TIAN Yonglin2,3, LI Guoqiang1, WANG Kunfeng1, LI Dazi1
1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029; 2. Department of Automation, University of Science and Technology of China, Hefei 230022; 3. The State Key Laboratory of Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract：Three-dimensional(3D) object detection plays a critical role in the fields of autonomous driving and robotics, since deep learning methods can offer possible solutions for accurate object detection, especially convolutional neural networks. The research progresses of convolutional neural network-based 3D object detection are reviewed comprehensively. Firstly, the practical value, basic procedures and challenges of 3D object detection are summarized. Next, the preliminary knowledge of convolutional neural networks, typical 2D object detection network structures, some widely-used open source datasets and point cloud representations is introduced. Then, progresses on the application of convolutional neural networks in 3D object detection are presented, and the methods are sorted out and analyzed according to different data modalities and method commonalities. Finally, issues in the existing research of 3D object detection are discussed, and future research trends are prospected.
王亚东, 田永林, 李国强, 王坤峰, 李大字. 基于卷积神经网络的三维目标检测研究综述[J]. 模式识别与人工智能, 2021, 34(12): 1103-1119.
WANG Yadong, TIAN Yonglin, LI Guoqiang, WANG Kunfeng, LI Dazi. 3D Object Detection Based on Convolutional Neural Networks: A Survey. , 2021, 34(12): 1103-1119.
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