A 3D Visibility Analysis Method for Reducing the Influence of Point Cloud Voids from an Indoor Perspective
Visibility refers to the spatial extent that an observer can see from a specific location under certain environmental conditions.Existing visibility analysis methods,based on point cloud data,mainly focus on the outdoor perspective and do not fully consider the problem of point cloud voids.To fill the gaps in the current indoor visibility research,this paper proposes an indoor 3D visibility analysis method based on point cloud data from UAVs and ground survey data.Our method reduces the influence of point cloud voids and aims to improve the accuracy of the visibility analysis.The method presented in this paper is divided into three parts:data preprocessing,visual space construction,and visual distance correction.In the data preprocessing stage,UAV point cloud data is used to extract the building roof contour and DEM,which are utilized for subsequent visual distance correction.Ground-based point cloud data is then used to extract the center points of the windows on the building façade,which serve as observation viewpoints.In the viewspace construction stage,the viewpoint is taken as the center,and the theoretical visual distance is taken as the radius.Points within the theoretical visual distance are selected to construct a hemispherical viewspace.The points in the visual field space are projected onto the depth image to form the visual field image.The visual field of view is then calculated,and the visual range of the field of view space is analyzed to simulate the external scene observed through the window in the room.During this process,point cloud voids can lead to errors in the visual range.Specifically,when the positions of the scanning center and the viewpoint differ significantly,missing parts may appear in the visible space,and the pass-through errors may also occur near the viewpoint due to insufficient point cloud density.In the sight distance correction stage,this paper categorizes the viewspace using spatial attributes and proposes a sight distance correction method to attenuate the effect of point cloud voids on the volume of the visible space by considering the feature continuity and occlusion relationship within different categories of the viewspace.Finally,visibility analysis is conducted using the visual space volume index at the viewpoint and is compared with the existing voxel-based and surface-based visibility analysis methods,as well as a manual evaluation.The DTW distances for comparisons are 48 247,and 240,respectively.The results show that the proposed method has better consistency with manual evaluation results.It can analyze the visibility of indoor environments more effectively,and is also applicable to outdoor visibility analysis.This method provides a comprehensive and reliable visibility analysis strategy for urban planning,landscape design,and other related fields.
point cloud voidsindoor viewpointvisual field spacevisual field imagevisual distance correctionvisibility analysis
万方数据
维普
