三维点云场景数据获取及其场景理解关键技术综述
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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.
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.
引文
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[28]Yang Z X,Cheng X J,Li Q,et al.Segmentation of point cloud in tank of plane bulkhead type[J].Chinese Journal of Lasers,2017,44(10):1010006.杨泽鑫,程效军,李泉,等.平面舱壁类型的船舱点云分割方法[J].中国激光,2017,44(10):1010006.
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[32]Cheng X J,Cheng X L,Hu M J,et al.Buildings detection and contour extraction by fusion of aerial images and LIDAR point cloud[J].Chinese Journal of Lasers,2016,43(5):0514002.程效军,程小龙,胡敏捷,等.融合航空影像和LIDAR点云的建筑物探测及轮廓提取[J].中国激光,2016,43(5):0514002.
[33]Wolf D,Prankl J,Vincze M.Fast semantic segmentation of 3Dpoint clouds using a dense CRFwith learned parameters[C]∥IEEE International Conference on Robotics and Automation(ICRA),May 26-30,2015,Seattle,WA,USA.New York:IEEE,2015:4867-4873.
[34]Hackel T,Wegner J D,Schindler K.Fast semantic segmentation of 3Dpoint clouds with strongly varying density[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2016,3(3):177-184.
[35]Weinmann M,Jutzi B,Hinz S,et al.Semantic point cloud interpretation based on optimal neighborhoods,relevant features and efficient classifiers[J].ISPRSJournal of Photogrammetry and Remote Sensing,2015,105:286-304.
[36]Xiang B B,Yao J,Lu X H,et al.Segmentationbased classification for 3Durban point clouds[C]∥IEEE International Conference on Information and Automation(ICIA),August 1-3,2016,Ningbo,China.New York:IEEE,2016:172-177.
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[38]Ghamisi P,H9fle B.LiDAR data classification using extinction profiles and a composite kernel support vector machine[J].IEEE Geoscience and Remote Sensing Letters,2017,14(5):659-663.
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