Visibility analysis is a crucial geographic information processing method, used to assess the observable spatial range from a particular location under specific conditions. It has broad applications, such as urban planning, landscape design and environmental research. Laser scanning provides detailed and accurate 3D data for visibility analysis, called point cloud. However, certain deficiencies still exist in current point cloud-based visibility analysis methods. Few methods are developed from indoor perspective and the influence of point cloud void is rarely considered. Firstly, indoor viewpoints are generated based on the windows extracted from terrestrial laser scanning (TLS) point cloud and unmanned aerial vehicle (UAV) point cloud is used to provide 3D representation of the environment around the viewpoint. Then, a half-sphere viewshed is created for each indoor viewpoint to analyze the visible spatial range. In this step, a novel line-of-sight correction method is proposed based on the semantic information from UAV point cloud, to correct the biased observation distance resulted from point cloud voids. Lastly, the volume of visible space is calculated based on the corrected viewshed image, to evaluate the visibility of each indoor viewpoint. Our method is validated on two datasets. The results indicate that our method enables more accurate simulation of visible space compared with previous studies by considering the effect of point cloud void. The proposed method can support the urban design from the perspective of indoor visual perception and accurate calculation of other visual factors in visual quality evaluation.

中文翻译：

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使用无人机和 TLS 点云以及视线校正进行基于室内视图的可见性分析


可见性分析是一种重要的地理信息处理方法，用于评估特定条件下特定位置的可观测空间范围。它具有广泛的应用，例如城市规划、景观设计和环境研究。激光扫描为可见性分析提供详细且准确的 3D 数据，称为点云。然而，当前基于点云的可见性分析方法仍然存在一定的缺陷。很少有方法是从室内角度开发的，也很少考虑点云空洞的影响。首先，基于从地面激光扫描（TLS）点云提取的窗口生成室内视点，并使用无人机（UAV）点云提供视点周围环境的3D表示。然后，为每个室内视点创建半球视域来分析可见空间范围。在这一步中，提出了一种基于无人机点云语义信息的视线校正方法，以校正点云空洞导致的观测距离偏差。最后，根据校正后的视域图像计算可见空间体积，以评估室内各视点的可见度。我们的方法在两个数据集上进行了验证。结果表明，通过考虑点云空洞的影响，与之前的研究相比，我们的方法能够更准确地模拟可见空间。该方法可以从室内视觉感知的角度支持城市设计，并准确计算视觉质量评价中的其他视觉因素。