Abstract
Abstract. In this paper, we propose a method for panoramic point-cloud rendering-based polygon extraction from indoor mobile LiDAR data. Our aim was to improve region-based point-cloud clustering in modeling after point-cloud registration. First, we propose a pointcloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud. Next, we describe an experiment that was conducted to verify our methodology with an indoor mobile mapping system in an indoor environment. This experiment was wall-surface extraction using a rendered point-cloud from 64 viewpoints over a wide indoor area. Finally, we confirmed that our proposed methodology could achieve polygon extraction through point-cloud clustering from a complex indoor environment.
Highlights
Massive point-cloud acquisition is an effective approach for 3D modeling of unknown objects in an indoor environment (Zlatanova et al 2013)
We propose a point-cloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud
We have proposed a method for panoramic rendering-based polygon extraction from indoor mobile LiDAR data
Summary
Massive point-cloud acquisition is an effective approach for 3D modeling of unknown objects in an indoor environment (Zlatanova et al 2013). Edge-based and region-based clustering are often used to model unknown objects (Tsai et al 2010) These approaches focus on geometrical knowledge (Pu, et al 2009) and 2D geometrical restrictions, such as the depth from a platform (Zhou, et al 2008) and discontinuous point extraction on each scanning plane from the mobile mapping system (Denis et al 2010) to extract simple boundaries and features in urban areas. We focus on the region-based point clustering to extract a polygon from a massive point cloud, because it is difficult to estimate accurate edges from the point cloud acquired with a laser scanner. We propose a point-cloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud. We confirm that our proposed methodology can achieve polygon extraction through pointcloud clustering from a complex indoor environment
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
