文献类型：期刊文献

英文题名：Recent advances in 3D object detection based on RGB-D: A survey

作者：Wang, Yangfan[1,2];Wang, Chen[3];Long, Peng[4];Gu, Yuzong[1];Li, Wenfa[5]

第一作者：Wang, Yangfan

通讯作者：Gu, YZ[1];Li, WF[2]

机构：[1]Henan Univ, Sch Phys & Elect, Kaifeng, Peoples R China;[2]Chinese Acad Sci, Inst Semicond, Beijing, Peoples R China;[3]Beihang Univ, Sch Comp Sci & Engn, State Key Lab Software Dev Environm, Jiangxi Res Inst, Beijing 100191, Peoples R China;[4]YouSan Educ Technol Co Ltd, Beijing, Peoples R China;[5]Beijing Union Univ, Coll Robot, Beijing, Peoples R China

第一机构：Henan Univ, Sch Phys & Elect, Kaifeng, Peoples R China

通讯机构：[1]corresponding author), Henan Univ, Sch Phys & Elect, Kaifeng, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2021

卷号：70

外文期刊名：DISPLAYS

收录：;EI(收录号：20213610871493);Scopus(收录号：2-s2.0-85114266594);WOS:【SCI-EXPANDED(收录号:WOS:000703884000001)】；

语种：英文

外文关键词：Computer vision; RGB-D data; Deep learning; 3D object detection

摘要：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.