基于海量点云数据的大雁塔三维重建

发布时间：2018-06-27 12:15

本文选题：大雁塔 + 三维重建　；参考：《文物保护与考古科学》2017年03期

【摘要】：古建筑三维重建已成为历史遗迹保护与修复的重要内容。为了探讨三维激光扫描数据的获取流程、数据处理方法,以三维激光扫描获取的大雁塔海量点云数据为例,研究了古建筑三维重建中点云数据获取与处理的详细过程。联合Cyclone、AutoCAD和3D-Max软件的优点构建了大雁塔真实的横面、剖面、立面模型,其中重点研究了海量点云数据在Cyclone软件中分块建模的方法。结果表明:将拼接好的大雁塔海量点云数据分块建模,不仅加快了数据处理的速度,而且符合精度要求。在点云数据拼接时采用标靶拼接和同名点拼接相结合及测站间两两拼接之后再整体拟合平差的方法,减少了拼接中的误差,使每站拼接误差都不超过3cm。在建模过程中的墙体拟合是上万个点云经过平差计算得到,精度高达2mm。最后在3D-Max软件中组合、渲染和贴图,真实地构建了大雁塔三维立体模型。为古建筑数字化存档和后期的修缮工作提供了重要的依据。
[Abstract]:Three-dimensional reconstruction of ancient buildings has become an important part of the protection and restoration of historical sites. In order to discuss the process of acquiring 3D laser scanning data, the data processing method is discussed. Taking the massive point cloud data obtained by 3D laser scanning as an example, the detailed process of point cloud data acquisition and processing in 3D reconstruction of ancient buildings is studied. Combining the advantages of Cyclone AutoCAD and 3D-Max software, the real model of cross plane, section and elevation of Big Wild Goose Pagoda is constructed, in which the modeling method of mass point cloud data in Cyclone software is studied. The results show that the block modeling of the large amount of point cloud data of the big goose pagoda not only speeds up the speed of data processing, but also meets the requirement of accuracy. In the point cloud data splicing, the method of combining target splicing with the same name point splicing and fitting the adjustment between two test stations is adopted, which reduces the error in the splicing, and makes the splicing error of each station less than 3 cm. In the process of modeling, the fitting of the wall is obtained by the adjustment of tens of thousands of point clouds, and the accuracy is as high as 2 mm. Finally, the 3D model of the Big Wild Goose Pagoda is constructed by combining, rendering and mapping in 3D-Max software. It provides an important basis for the digital archiving of ancient buildings and the later repair work.
【作者单位】：西安科技大学测绘科学与技术学院;国家测绘地理信息局大地测量数据处理中心;北京则泰盛业科技发展有限公司;
【分类号】：K878.6;TN249;TP391.7

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