**1. Introduction**

Massive point-cloud acquisition is an effective approach for 3D modeling of unknown objects in various fields, such as urban mapping, indoor mapping, plant management, factory management, heritage documentation, and infrastructure asset inspection and management. In construction fields, base maps and 3D data are required for managing processes of construction, maintenance, rehabilitation, and replacement. Online maps, such as Google Maps and

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

OpenStreetMap, are useful for approximate construction surveys in urban areas. However, online maps are often insufficient for infrastructure inspection to recognize the details of natural features. Thus, base maps and 3D data should be prepared before inspection. Massive point-cloud data can be acquired with a terrestrial laser scanner, mobile mapping systems (MMSs), handheld laser scanners, and cameras using structure from motion (SfM) methodology. SfM is a methodology for reconstructing a scene using multiple cameras simultaneously from all available relative motions through key point detection, feature matching, motion estimation, triangulation, and bundle adjustment. In an open sky environment, aerial photogrammetry and SfM using an unmanned aerial vehicle (UAV) are more effective than groundbased scanning. On the other hand, when environments include natural obstacles, such as trees, a terrestrial laser scanner is more effective than a UAV or MMSs. In indoor navigation and building information modeling (BIM), floor maps and 3D data are also required. We expected terrestrial laser scanners and indoor MMSs to be adequate for colored point-cloud acquisition in an indoor environment.

fields, edge-based and region-based clustering are often applied [4]. These approaches use low-resolution point-cloud data that consist of tens of points or several hundred points per

Point Cloud Clustering Using Panoramic Layered Range Image

http://dx.doi.org/10.5772/intechopen.76407

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, such as aerial laser and vehicle-borne MMSs data. These approaches also focus on geometrical knowledge [5] and 2D geometrical restrictions, such as the depth from a platform [6] and discontinuous point extraction on each scanning plane from the MMSs [7] to extract features. In urban areas and indoor environments, although there are simple features consisting of lines and planes, there are many complex features consisting of curved lines and surfaces with unclear boundaries. Moreover, point-cloud data are generally acquired with a terrestrial and mobile laser scanner from many viewpoints and view angles for 3D modeling. Like the conventional approaches, range image processing is proposed to apply 2D restrictions with an interactive procedure in 3D plant modeling. However, viewpoints for range image render-

Thus, our aim was to improve region-based point-cloud clustering in modeling after pointcloud integration. We also focused on region-based point clustering to extract a polygon from a massive point cloud, because it is not easy to estimate accurate edges from point clouds acquired with a laser scanner. In region-based clustering, random sample consensus (RANSAC) [8] is a suitable approach for surface detection and estimation. However, local work space should be selected to improve performance in a surface estimation from a massive point cloud. Moreover, it is hard to determine whether a point lies inside or outside a surface

In this chapter, we first 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 three experiments to verify our methodology. The first experiment was a 3D edge and surface extraction for indoor modeling using an indoor MMS. The second experiment was a 3D edge and surface extraction for 3D bridge modeling using a terrestrial laser scanner. The third experiment was a 3D edge and surface extraction for ground surface and feature extraction using a terrestrial laser scanner. Even though the acquired data had low homogeneity of spatial point density, these experiments confirmed that a terrestrial laser scanner could cover complex surfaces, including flat surfaces, slopes, and steps. We also confirmed that our proposed methodology could achieve point-cloud clustering to extract these features

Our proposed processing flow for point-cloud clustering is shown in **Figure 2**. First, we register and integrate point-cloud data acquired from a viewpoint. Next, the point-cloud data are projected into the image space with translation, view angle, and resolution parameters in "Panoramic multilayered range image generation with point cloud rendering" to generate several range images. Then, normal vectors around each projected point are estimated using the 3D coordinate values of the point cloud in "Normal vector estimation in panoramic multilayered range image." Next, edges are extracted from depth images generated in the panoramic

m2

ing are limited to data acquisition points.

with conventional RANSAC.

from complex environments.

**2. Methodology**

Moreover, point-cloud clustering is an essential technique for modeling massive point clouds. **Figure 1** shows an example of point-cloud clustering using a terrestrial laser scanner data acquired in an indoor environment.

There are three clustering approaches in point-cloud clustering, namely model-based clustering [1], edge-based clustering [2], and region-based clustering [3]. Model-based clustering is a 3D model preparation approach. The model-based clustering requires 3D models such as CAD models to estimate simple objects or point clusters from the point cloud. In 3D industrial modeling, standardized objects, such as pipes, boxes, and parts, are prepared as CAD models in advance. Although the model-based clustering is suitable for modeling known objects such as the standardized objects, the model-based clustering is unsuitable for modeling unknown objects such as complex and natural objects. On the other hand, in modeling unknown objects, such as buildings and roads in geoinformatics and civil engineering

**Figure 1.** Point-cloud clustering: colored point cloud (left image) and clustered point cloud (right image).

fields, edge-based and region-based clustering are often applied [4]. These approaches use low-resolution point-cloud data that consist of tens of points or several hundred points per m2 , such as aerial laser and vehicle-borne MMSs data. These approaches also focus on geometrical knowledge [5] and 2D geometrical restrictions, such as the depth from a platform [6] and discontinuous point extraction on each scanning plane from the MMSs [7] to extract features. In urban areas and indoor environments, although there are simple features consisting of lines and planes, there are many complex features consisting of curved lines and surfaces with unclear boundaries. Moreover, point-cloud data are generally acquired with a terrestrial and mobile laser scanner from many viewpoints and view angles for 3D modeling. Like the conventional approaches, range image processing is proposed to apply 2D restrictions with an interactive procedure in 3D plant modeling. However, viewpoints for range image rendering are limited to data acquisition points.

Thus, our aim was to improve region-based point-cloud clustering in modeling after pointcloud integration. We also focused on region-based point clustering to extract a polygon from a massive point cloud, because it is not easy to estimate accurate edges from point clouds acquired with a laser scanner. In region-based clustering, random sample consensus (RANSAC) [8] is a suitable approach for surface detection and estimation. However, local work space should be selected to improve performance in a surface estimation from a massive point cloud. Moreover, it is hard to determine whether a point lies inside or outside a surface with conventional RANSAC.

In this chapter, we first 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 three experiments to verify our methodology. The first experiment was a 3D edge and surface extraction for indoor modeling using an indoor MMS. The second experiment was a 3D edge and surface extraction for 3D bridge modeling using a terrestrial laser scanner. The third experiment was a 3D edge and surface extraction for ground surface and feature extraction using a terrestrial laser scanner. Even though the acquired data had low homogeneity of spatial point density, these experiments confirmed that a terrestrial laser scanner could cover complex surfaces, including flat surfaces, slopes, and steps. We also confirmed that our proposed methodology could achieve point-cloud clustering to extract these features from complex environments.
