Recent advances in 3D object detection based on RGB-D: A survey |
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| Affiliation: | 1. School of Physics and Electronics, Henan University, China;2. Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China;3. School of Computer Science and Engineering, State Key Laboratory of Software Development Environment, Jiangxi Research Institute, Beihang University, 100191, China;4. YouSan Educational Technology Co., Ltd, China;5. College of Robotics, Beijing Union University, Beijing, China;1. University of Southern Mississippi, Hattiesburg, MS 39406, USA;2. Department of Electrical Engineering, Indian Institute of Technology Jammu, Nagrota, Jammu 181221, India;3. Department of Electronics and Communication Engineering, National Institute of Technology Goa, Ponda, Goa 403401, India;4. Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India;1. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;2. Science and Technology Information Department of Tianjin Public Security Bureau, Tianjin 300384, China;3. School of Computer Science, Beihang University, Beijing 100191, China;4. School of Management, Guangzhou University, Guangdong 510006, China;1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China;2. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China;1. School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China;2. School of Engineering, Beijing University of Technology, Beijing 101303, China |
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| Abstract: | 3D object detection is a critical part of environmental perception systems and one of the most fundamental tasks in understanding the 3D visual world, which benefit a series of downstream real-world applications. RGB-D images include object texture and semantic information, as well as depth information describing spatial geometry. Recently, numerous 3D object detection models for RGB-D images have been proposed with excellent performance, but summaries in this area are still absent. To stimulate future research, this paper provides a detailed analysis of current developments in 3D object detection methods for RGB-D images to motivate future research. It covers three major parts, including background on 3D object detection, RGB-D data details, and comparative results of state-of-the-art methods on several publicly available datasets, with an emphasis on contributions, design ideas, and limitations, as well as insightful observations and inspiring future research directions. |
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| Keywords: | Computer vision RGB-D data Deep learning 3D object detection |
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