摘要
场景理解是信息科学里的重要研究内容,而三维(3D)数据相比于二维(2D)数据有着众多优势。目前点云的获取有多种方式,且不同获取方式的点云具有不同的特点,此外,基于点云的3D场景理解中的关键技术研究还没有完整、系统的综述。为此,总结了不同方式的点云获取方法,并对不同的点云数据及相关数据库进行对比分析。基于目前3D场景理解的研究进展,针对3D场景理解中的点云滤波、特征提取与点云分割和点云语义分割等技术进行了对比分析与总结。通过对近些年国内外文献的结论进行梳理,凝练出3D场景理解关键技术中存在的问题,并对3D场景理解问题的发展趋势做了展望。基于点云的3D场景理解因其数据的丰富性而被广泛应用在众多领域中,但是目前基于3D点云的场景理解效果,尤其是针对具有颜色信息的激光点云的场景理解,还有众多内容有待深入研究。
Scene understanding is an important research content in information science.Compared with the twodimensional(2 D)data,the three-dimensional(3D)data has many advantages.At present,there are many ways to acquire the point clouds,and meanwhile the point clouds with different acquisition methods have different characteristics.In addition,there lacks a complete and systematic research review on the key techniques for 3D scenes understanding.Thus,the different methods for point cloud acquisition are summarized,and the different point cloud data and related databases are compared and analyzed as well.Based on the current research progress of3 Dscene understanding,the techniques for point cloud filtering,feature extraction,point cloud segmentation,and point cloud semantic segmentation in 3 Dscene understanding are compared and summarized.By the summary of the domestic and foreign literatures published in recent years,the problems occurred in the key technologies for 3 D scene understanding are condensed,and the development trend of the 3 D scene understanding problems is prospected.The 3D scene understanding based on point clouds is widely used in many fields due to its richness of data.However,as for the scene understanding effect of 3D point clouds,especially the scene understanding of laser point clouds with color information,there are still many contents needed to be investigated in depth.
作者
李勇
佟国峰
杨景超
张立强
彭浩
高华帅
Li Yong;Tong Guofeng;Yang Jingchao;Zhang Liqiang;Peng Hao;Gao Huashuai(College of Information Science and Engineering,Northeastern University,Shenyang,Liaoning 110819,China;Department of Electrical and Information Engineering,Hebei Jiaotong Vocational and Technical College,Shijiazhuang,Hebei 050091,China;The State Key Laboratory of Remote Sensing Science,Beijing Normal University,Beijing 100875,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第4期21-34,共14页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61175031)
国家863计划(2012AA041402)
关键词
机器视觉
三维点云
场景理解
语义分割
machine vision
3D point cloud
scene understanding
semantic segmentation
