Point Cloud Clustering for 3D Modeling Assistance Using a Panoramic Layered Range Image
Point Cloud Clustering for 3D Modeling Assistance Using a Panoramic Layered Range Image
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DOI：
10.18005/jrst0103001
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发表时间：
2013-11
影响因子：
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通讯作者：
M. Nakagawa
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文献类型：
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作者：
M. Nakagawa
我们的目标是改进基于区域的点云聚类建模后的点云集成。首先，我们提出了一个点云聚类方法的全景分层深度图像生成的点基于点的渲染从一个巨大的点云。接下来，我们使用地面LiDAR进行了两个实验来验证我们的方法。这些实验的结果证实，我们提出的方法可以实现点云聚类提取任意功能，从复杂的环境，包括平面，斜坡和石阶的3D映射。关键词点绘制;点云聚类;地面激光扫描;表面提取;三维边缘提取
Our aim is to improve region-based point cloud clustering in modeling after point cloud integration. First, we proposed a point cloud clustering methodology on a panoramic layered range image generated with point-based rendering from a massive point cloud. Next, we conducted two experiments using terrestrial LiDAR to verify our methodology. The results of these experiments confirmed that our proposed methodology can achieve point cloud clustering to extract arbitrary features from complex environments including flat surfaces, slopes and stone steps for 3D mapping. KeywordsPoint-based Rendering; Point-Cloud Clustering; Terrestrial Laser Scanning; Surface Extraction; 3D Edge Extraction