Abstract
3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level) of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework.
Highlights
Urban planning, heritage documentation, military simulation, airflow analysis, transportation management, and Building Information Modeling (BIM) are among the applications that need accurate 3D models of the sites in question
We introduce the conceptual basis and implementation details for the first four steps of the processing framework in Figure 1
Airborne Laser Scanner Dataset—ALS: This dataset is captured by an Optech ALTM 3100 over an urban area that includes planar roofs, roads, and trees/bushes
Summary
Heritage documentation, military simulation, airflow analysis, transportation management, and Building Information Modeling (BIM) are among the applications that need accurate 3D models of the sites in question. Optical imaging and laser scanning systems are the two leading data acquisition modalities for 3D model generation. On the other hand, are capable of directly providing accurate point clouds at high density. To allow for the derivation of semantic information, image and laser-based point clouds need to undergo a sequence of data processing steps to meet the demands of Digital Building. Model—DBM—generation, urban planning [1], as-built mapping of industrial sites, transportation infrastructure systems [2], cultural heritage documentation [3], and change detection. Point cloud segmentation according to pre-defined criteria is one of the initial steps in the data processing chain
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
