**5. Conclusion**

We have focused on improving region-based point-cloud clustering in 3D modeling after pointcloud integration. We have also focused on region-based point clustering to extract a polygon from a massive point cloud, because it is difficult to estimate accurate edges from point clouds acquired with a laser scanner. First, we proposed a point-cloud clustering methodology on a panoramic layered range image generated from a massive point cloud with point-based rendering. Next, we conducted three experiments using laser scanning data to verify our methodology. The first experiment was 3D edge and surface extraction for indoor modeling using an indoor MMS. The second experiment was 3D edge and surface extraction for 3D bridge modeling using a terrestrial laser scanner. The third experiment was 3D edge and surface extraction for groundsurface and feature extraction using a terrestrial laser scanner. The results of these experiments confirm that our proposed methodology can achieve point-cloud clustering to extract features such as flat surfaces, slopes, and steps from complex environments in practical processing times.
