3D Object Shape Reconstruction from Underwater Multibeam Data and Over Ground Lidar Scanning

Abstract

The technologies of sonar and laser scanning are an efficient and widely used source of spatial information with regards to underwater and over ground environment respectively. The measurement data are usually available in the form of groups of separate points located irregularly in three-dimensional space, known as point clouds. This data model has known disadvantages, therefore in many applications a different form of representation, i.e. 3D surfaces composed of edges and facets, is preferred with respect to the terrain or seabed surface relief as well as various objects shape. In the paper, the authors propose a new approach to 3D shape reconstruction from both multibeam and LiDAR measurements. It is based on a multiple-step and to some extent adaptive process, in which the chosen set and sequence of particular stages may depend on a current type and characteristic features of the processed data. The processing scheme includes: 1) pre-processing which may include noise reduction, rasterization and pre-classification, 2) detection and separation of objects for dedicated processing (e.g. steep walls, masts), and 3) surface reconstruction in 3D by point cloud triangulation and with the aid of several dedicated procedures. The benefits of using the proposed methods, including algorithms for detecting various features and improving the regularity of the data structure, are presented and discussed. Several different shape reconstruction algorithms were tested in combination with the proposed data processing methods and the strengths and weaknesses of each algorithm were highlighted.

Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS

“…After the data has been regularized and denoised, the contents of the point cloud are classified into several categories, including ground, objects (e.g., buildings), and greenery. The classification algorithm first attempts to detect large terrestrial objects and assign them to a new class, in order to separate them from ground points (using an approach that is similar to the one applied to underwater objects in [24]). The class assignment of each detected object is then corrected based on the object's colour data.…”

“…As a result, the methods of automatic shape recovery and the construction of more composed geometric models of spatial objects from point cloud data are crucial with respect to many applications of LiDAR measurements, and they have been the subject of extensive research for over a decade [23]. In consequence, several methods exist and different approaches are applied, depending on the type of scanned objects and application specifics; however, attempts to create a more universal approach to the problem are also carried out [24,25]. Thus, it may be expected that, in the near future, tools for shape recovery and higher order geometric model construction should become a standard element of systems that are dedicated to the management and dissemination of LiDAR point cloud data.…”