基于3D基元拟合的复杂屋顶点云三维重建  

Complex Roof Structure Reconstruction by 3D Primitive Fitting from Point Clouds

作　　者：张文元[1] 陈江媛 谈国新[1] ZHANG Wenyuan;CHEN Jiangyuan;TAN Guoxin(National Research Center of Cultural Industries,Central China Normal University,Wuhan 430079,China)

机构地区：[1]华中师范大学国家文化产业研究中心,武汉430079

出　　处：《地球信息科学学报》2023年第8期1531-1545,共15页Journal of Geo-information Science

基　　金：国家自然科学基金项目(41801295);国家文化和旅游科技创新工程项目(2019-008)。

摘　　要：几何语义一体化三维建筑物模型是智慧城市建设的重要基础数据,有利于促进建筑设施的精细化管理和智能化应用。当前基于点云的三维重建算法大多关注简单屋顶结构的几何模型构建,忽略了模型的语义表达,且基于数据驱动方法的重建结果容易受噪声影响,存在几何和拓扑错误。为了解决复杂屋顶高精度三维重建难题,本文提出一种基于3D基元拟合的复杂屋顶点云三维自动化重建算法。首先,设计了一套可参数化表达的建筑物3D基元库,包含简单和复杂屋顶。其次,通过点云分割和屋顶拓扑图比较来识别点云对应的基元类型。然后,提出了一种点云与3D基元整体拟合的目标优化函数,采用序列二次规划算法估计基元的正确参数。最后,利用城市地理标记语言(City Geography Markup Language,CityGML)构建几何、语义和拓扑一体化表达的三维模型。采用几种不同屋顶风格的建筑物点云数据进行实验,定性和定量对比分析结果表明本文方法能够高效生成几何和拓扑均正确的CityGML模型,对噪声和局部点云缺失具有一定的鲁棒性,有利于促进几何语义一体化建筑物模型快速自动化构建。Geometric and semantic integration of 3D building models are important infrastructure data for smart city,they are conducive for promoting the refined management and intelligent application of building facilities.However,most of the existing point cloud-based modeling methods focus on the reconstruction of geometric models with simple roof structure,and semantic and topological relations are ignored.Moreover,these methods are sensitive to noise,which are difficult to assure topological consistency and geometric accuracy.To solve these problems,this paper proposes a 3D primitive fitting algorithm for automatically reconstructing building models with complex roof structure from point clouds.Firstly,a 3D building primitive library is designed,including various 3D building primitives with simple and complex roof types.Secondly,an individual building point cloud input is segmented into multiple planes using RANSAC algorithm.The Roof Topology Graph(RTG)is then generated according to the relationship of roof planes,and the roof type of point cloud is subsequently recognized by comparison of RTG between point cloud and building primitives.Thirdly,the reconstruction is formulated as an optimization problem that minimizes the Point-to-Mesh Distance(PMD)between the point cloud and the candidate meshed building primitive.The sequential quadratic programming optimization algorithm with necessary constraints is adopted to perform holistically primitive fitting,so as to estimate the shape and position parameters of a 3D primitive.Finally,the parameterized model is automatically converted into City Geography Markup Language(CityGML)building model based on the prior 3D building primitive.The generated CityGML LoD2(second level of detail)models are different from mesh models created by conventional building modeling methods,which are represented with geometric,semantic,and topological information.To evaluate the quality and performance of the proposed approach,airborne lidar and photogrammetric building point clouds with different

关 键 词：建筑物 复杂屋顶 点云 三维重建 3D基元 城市地理标记语言 语义 模型驱动 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TU198[自动化与计算机技术—计算机科学与技术]