面向智能化测绘的城市地物三维提取

doi:10.11947/j.AGCS.2022.20220183

摘要/Abstract

摘要： 精准有效的三维时空信息是新型基础设施建设、实景三维中国、自然资源管理与监测等国家重大需求不可或缺的重要支撑。测绘装备的快速发展提升了点云和影像获取的便捷性,为智能化测绘提供了一种全新技术手段。然而,如何从点云、影像中智能地提取三维时空信息仍然面临诸多困境。本文围绕城市场景中地物目标智能化提取的需求,重点阐述点云位置精度改善、点云与全景影像融合、部件级典型地物要素精细提取、软件研发与工程实践4个方面的关键技术与研究进展,为实景三维中国建设等提供关键支持。

关键词: 三维信息提取, 点云, 特征融合, 智能化测绘, 人工智能

Abstract: 3D spatial-temporal information is indispensable for the major national needs of new infrastructure construction, digital twin cities, natural resource management and monitoring. The rapid development of surveying and mapping equipment is beneficial to improve the convenience of point cloud and image acquisition, providing a new technical means for smart surveying and mapping. However, how to intelligently extract 3D spatial-temporal information from point cloud and image is still facing challenges. Surrounding the core of the smart surveying and mapping, this paper focuses on the key technologies and research progress of point cloud location accuracy improvement, point cloud and panoramic image fusion, component-level urban objects extraction, software development and practice.

Key words: 3D geospatial information extraction, point cloud, feature fusion, smart surveying and mapping, artificial intelligence

中图分类号:

P237

杨必胜, 陈驰, 董震. 面向智能化测绘的城市地物三维提取[J]. 测绘学报, 2022, 51(7): 1476-1484.

YANG Bisheng, CHEN Chi, DONG Zhen. 3D geospatial information extraction of urban objects for smart surveying and mapping[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1476-1484.

参考文献

[1] 陈俊勇.面向数字中国建设中国的现代测绘基准——对我国"十五"大地测量工作的思考和建议[J].测绘通报, 2001(3):1-3. CHEN Junyong.Digital China-oriented construction of China's modern geodetic datum[J]. Bulletin of Surveying and Mapping, 2001(3):1-3.
[2] 陈军,刘万增,武昊,等.智能化测绘的基本问题与发展方向[J].测绘学报, 2021, 50(8):995-1005. DOI:10.11947/j.AGCS.2021.20210235. CHEN Jun, LIU Wanzeng, WU Hao, et al. Smart surveying and mapping:fundamental issues and research agenda[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):995-1005. DOI:10.11947/j.AGCS.2021.20210235.
[3] 张永生,张振超,童晓冲,等.地理空间智能研究进展和面临的若干挑战[J].测绘学报, 2021, 50(9):1137-1146. DOI:10.11947/j.AGCS.2021.20200420. ZHANG Yongsheng, ZHANG Zhenchao, TONG Xiaochong, et al. Progress and challenges of geospatial artificial intelligence[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9):1137-1146. DOI:10.11947/j.AGCS.2021.20200420.
[4] 杨元喜,杨诚,任夏. PNT智能服务[J].测绘学报, 2021, 50(8):1006-1012. DOI:10.11947/j.AGCS.2021.20210051. YANG Yuanxi, YANG Cheng, REN Xia. PNT intelligent services[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1006-1012. DOI:10.11947/j.AGCS.2021.20210051.
[5] 龚健雅,许越,胡翔云,等.遥感影像智能解译样本库现状与研究[J].测绘学报, 2021, 50(8):1013-1022. DOI:10.11947/j.AGCS.2021.20210085. GONG Jianya, XU Yue, HU Xiangyun, et al. Status analysis and research of sample database for intelligent interpretation of remote sensing image[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1013-1022. DOI:10.11947/j.AGCS.2021.20210085.
[6] 张继贤,李海涛,顾海燕,等.人机协同的自然资源要素智能提取方法[J].测绘学报, 2021, 50(8):1023-1032. DOI:10.11947/j.AGCS.2021.20210102. ZHANG Jixian, LI Haitao, GU Haiyan, et al. Study on man-machine collaborative intelligent extraction for natural resource features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1023-1032. DOI:10.11947/j.AGCS.2021.20210102.
[7] 李志林,刘万增,徐柱,等.时空数据地图表达的基本问题与研究进展[J].测绘学报, 2021, 50(8):1033-1048.DOI:10.11947/j.AGCS.2021.20210072. LI Zhilin, LIU Wanzeng, XU Zhu, et al. Cartographic representation of spatio-temporal data:fundamental issues and research progress[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1033-1048. DOI:10.11947/j.AGCS.2021.20210072.
[8] 史文中,张敏.人工智能用于遥感目标可靠性识别:总体框架设计、现状分析及展望[J].测绘学报, 2021, 50(8):1049-1058. DOI:10.11947/j.AGCS.2021.20210095. SHI Wenzhong, ZHANG Min. Artificial intelligence for reliable object recognition from remotely sensed data:overall framework design, review and prospect[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1049-1058. DOI:10.11947/j.AGCS.2021.20210095.
[9] 杨必胜,韩旭,董震.适用于城市场景大规模点云语义标识的深度学习网络[J].测绘学报, 2021, 50(8):1059-1067. DOI:10.11947/j.AGCS.2021.20210093. YANG Bisheng, HAN Xu, DONG Zhen. A deep learning network for semantic labeling of large-scale urban point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1059-1067. DOI:10.11947/j.AGCS.2021.20210093.
[10] 张永军,万一,史文中,等.多源卫星影像的摄影测量遥感智能处理技术框架与初步实践[J].测绘学报, 2021, 50(8):1068-1083.DOI:10.11947/j.AGCS.2021.20210079. ZHANG Yongjun, WAN Yi, SHI Wenzhong, et al. Technical framework and preliminary practices of photogrammetric remote sensing intelligent processing of multi-source satellite images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1068-1083. DOI:10.11947/j.AGCS.2021.20210079.
[11] 郝彤,王晓峰,冯甜甜,等.地球系统多尺度关键区域与关键过程的智能化测绘[J].测绘学报, 2021, 50(8):1084-1095. DOI:10.11947/j.AGCS.2021.20210109. HAO Tong, WANG Xiaofeng, FENG Tiantian, et al. Intelligent and multi-scale surveying of key areas and processes of the Earth system[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1084-1095. DOI:10.11947/j.AGCS.2021.20210109.
[12] 张广运,张荣庭,戴琼海,等.测绘地理信息与人工智能2.0融合发展的方向[J].测绘学报, 2021, 50(8):1096-1108. DOI:10.11947/j.AGCS.2021.20210200. ZHANG Guangyun, ZHANG Rongting, DAI Qionghai, et al. The direction of integration surveying and mapping geographic information and artificial intelligence 2.0[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1096-1108. DOI:10.11947/j.AGCS.2021.20210200.
[13] 吴立新,李佳,苗则朗,等.冰川流域孕灾环境及灾害的天空地协同智能监测模式与方向[J].测绘学报, 2021, 50(8):1109-1121. DOI:10.11947/j.AGCS.2021.20210107. WU Lixin, LI Jia, MIAO Zelang, et al. Pattern and directions of spaceborne-airborne-ground collaborated intelligent monitoring on the geo-hazards developing environment and disasters in glacial basin[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1109-1121. DOI:10.11947/j.AGCS.2021.20210107.
[14] 陶超,阴紫薇,朱庆,等.遥感影像智能解译:从监督学习到自监督学习[J].测绘学报, 2021, 50(8):1122-1134. DOI:10.11947/j.AGCS.2021.20210089. TAO Chao, YIN Ziwei, ZHU Qing, et al. Remote sensing image intelligent interpretation:from supervised learning to self-supervised learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1122-1134. DOI:10.11947/j.AGCS.2021.20210089.
[15] BROUWER T, KOEVA M, VAN OOSTEROM P, et al. Smart surveyors:developments and trends from the FIG working week 2020[J]. GIM International, 2020,232:1547-1558.
[16] YANG B, LI Y, ZOU X, et al. Amarker-free calibration method for mobile laser scanning point clouds correction[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020:347-354.
[17] DONG Zhen, YANG Bisheng, LIU Yuan, et al. A novel binary shape context for 3D local surface description[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130:431-452.
[18] LI Jianping, YANG Bisheng, CHEN Chi, et al. Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 136:41-57.
[19] TAO A, SAPRA K, CATANZARO B. Hierarchical multi-scale attention for semantic segmentation[J]. 2020.
[20] FAN Wen, YANG Bisheng, DONG Zhen, et al. Confidence-guided roadside individual tree extraction for ecological benefit estimation[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102:102368.
[21] LEPETIT V, MORENO-NOGUER F, FUA P. EPnP:an accurate O (n) solution to the PnP problem[J]. International Journal of Computer Vision, 2008, 81(2):155-166.
[22] QI C R, LIU Wei, WU Chenxia, et al. Frustum PointNets for 3D object detection from RGB-D data[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT, USA:IEEE, 2018:918-927.
[23] SHAO Shuai, LI Zeming, ZHANG Tianyuan, et al. Objects365:a large-scale, high-quality dataset for object detection[C]//Proceedings of the IEEE/CVF international conference on computer vision.Seoul, Korea (South):IEEE, 2019:8429-8438.
[24] ZHU Zhe, LIANG Dun, ZHANG Songhai, et al. Traffic-sign detection and classification in the wild[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.Las Vegas, NV, USA:IEEE, 2016:2110-2118.
[25] CAI Zhaowei, VASCONCELOS N. Cascade R-CNN:delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT, USA:IEEE, 2018:6154-6162.
[26] YANG B, WANG J, CLARK R, et al. Learning object bounding boxes for 3D instance segmentation on point clouds[C]//Proceedings of the Advances in neural information processing systems.Vancouver,Canada:IEEE,2019, 32.
[27] QI C R, YI L, SU H, et al. Pointnet++:Deep hierarchical feature learning on point sets in a metric space[C]//Proceedings of the Advances in neural information processing systems.Long Beach,CALIFORNIA,USA:IEEE,2017, 30.
[28] WENG Xinshuo, WANG Jianren, HELD D, et al. 3D multi-object tracking:a baseline and new evaluation metrics[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas, NV, USA:IEEE, 2021:10359-10366.