Reconstruction At LOD-2 Level Of Building Models By Means Of Contour Features From Oblique Photogrammetry

The levels of detail of three-dimensional building models usually include four typical models from LOD-0 to LOD-3,with different geometric and semantic information for different levels of application requirements.In particular,the LOD-2 model that can distinguish from the roof and fa(?)ade structure of a building constitutes the skeleton structure of Smart City and has the most extensive application in urban planning and construction management.Due to the fa(?)ade visibility,intuitive expression and multi-view redundancy,the oblique photogrammetry has become the main data source for large-scale urban LOD-2reconstruction.The existing LOD-2 reconstruction methods based on oblique photogrammetry suffer from: 1)the technical bottleneck problems of difficult distinction and inaccurate extraction;2)topological reconstruction problems caused by data quality issues such as high noise,severe occlusion,and missing semantic information,resulting in low automation of LOD-2 model reconstruction and poor model quality.To overcome these,the paper makes full use of the available crowdsourcing contour features of building and intensively investigates the reconstruction at levels of detail of building models by means of contour features from oblique photogrammetry,with the aim of solving the problem of identification,extraction,and levels of detail model reconstruction for the buildings with multi-type and multi-shape.The specific research is as follows:(1)Multi-entity registration method for existing building contour features and oblique photogrammetric point cloudDifferences in the source and presentation between existing building contour feature data and oblique photogrammetric point cloud,inevitably lead to location bias.The accurate registration of the two is therefore extremely essential.The paper proposes a face-line-point multi-entity registration method for building contour features and oblique photogrammetric point clouds,using planar features as primitives to extract building fa(?)ade contour features from point clouds;using line features as primitives to construct line junction features,and adopting a RANSAC-based matching method to search for corresponding line junction features between building contour data and point cloud;using points as primitives,the transformation matrix is computed using a global least-squares optimization method for the intersections of line structure features with correspondence,achieving accurate registration of building contour data and point clouds.(2)Reliable plane extraction method of buildings via global normal refinement from noisy oblique photogrammetric point cloud.Models of urban buildings are often complex,but they can be assumed to be a combination of multiple simple planes,so it is fundamental to extract reliable and accurate planar primitives from building point cloud.However,duo to the problem of high noise in oblique photogrammetric point clouds,traditional regional growth method is inefficient in extracting complete planar primitives.Therefore,we propose a reliable plane extraction method of buildings via global normal refinement from noisy oblique photogrammetric point cloud.1)constructing supervoxels with strict planar features using similarity clustering;2)introducing the concept of maximum planar support regions and merging adjacent voxels to construct maximum planar support regions;3)adopting the point cloud local-global consistency spatial constraint method to transform local features into global features and optimize the point cloud normal;4)finally using the maximum planar support region guided planar extraction method to achieve accurate extraction of building planes.(3)Detailed structure optimization method for buildings guided by fa(?)ade structures of contour features.The finite parametric primitive models of existing model-driven approaches are difficult to support modeling complex environments,while data-driven approaches are constrained by point clouds noise,even the simplest plane primitives are difficult to be extracted fully and accurately,much less accurately recover the regular structure between the primitives.Therefore,we propose a detailed structure optimization method for buildings guided by fa(?)ade structures of contour features.1)constrained by the crowdsource building contour data,the building edge information with semantic rules is constructed based on the accurately extracted building vertical planes;2)generating a corresponding regular fa(?)ade structure for each edge by distance similarity clustering of the profiles and BIP optimization;3)finally,a procedural modeling approach is used to recovery the fine structure of the building and reconstruct the LOD-2 building model,using the building edge lines as basis and their fa(?)ade structures as guide.