Abstract. Videogrammetry is a technique to generate point clouds by using video frame sequences. It is a branch of photogrammetry that offers an attractive capabilities and make it an interesting choice for a 3D data acquisition. However, different camera input and specification will produce different quality of point cloud. Thus, it is the aim of this study to investigate the quality of point cloud that is produced from various camera input and specification. Several devices are using in this study such as Iphone 5s, Iphone 7+, Iphone X, Digital camera of Casio Exilim EX-ZR1000 and Nikon D7000 DSLR. For each device, different camera with different resolution and frame per second (fps) are used for video recording. The videos are processed using EyesCloud3D by eCapture. EyesCloud3D is a platform that receive input such as videos and images to generate point clouds. 3D model is constructed based on generated point clouds. The total number of point clouds produced is analyzed to determine which camera input and specification produce a good 3D model. Besides that, factor of generating number of point clouds is analyzed. Finally, each camera resolution and fps is suggested for certain applications based on generated number of point cloud.
University campuses consists of many buildings within a large area managed by a single organization. Like 3D city modeling, a 3D model of campuses can be utilized to provide a better foundation for planning, navigation and management of buildings. This study approaches 3D modeling of the UTM campus by utilizing data from aerial photos and site observations. The 3D models of buildings were drawn from building footprints in SketchUp and converted to CityGML using FME software. The CityGML models were imported into a geodatabase using 3DCityDB and visualized in Cesium. The resulting 3D model of buildings was in CityGML format level of detail 2, consisting of ground, wall and roof surfaces. The 3D models were positioned with real-world coordinates using the geolocation function in SketchUp. The non-spatial attributes of the 3D models were also stored in a database managed by PostgreSQL. While the methodology demonstrated in this study was found to be able to create LoD2 building models. However, issues of accuracy arose in terms of building details and positioning. Therefore, higher accuracy data, such as point cloud data, should produce higher LoD models and accurate positioning.
Abstract. Adjacencies between objects provides the most basic connectivity information of objects. This connectivity information provides support for more complex 3D spatial analysis such as 3D navigation, nearest neighbour and others. In 3D models, the connectivity information is maintained by building a comprehensive 3D topology. As the international standard for 3D city models, CityGML employs a simple XML links mechanism that references related entities to each other as a means of maintaining topological information. This method fulfils the purpose of relating connected entities but, it does not describe how the entities are related or in other words its adjacencies. In this study, a 3D topological data structure was utilised to preserve topological primitives and maintain connectivity information for CityGML datasets of buildings in LoD2. The adjacencies tested in this study were based on the topological links maintained by the Compact Abstract Cell Complexes 3D topological data structure. Four types of adjacencies were tested which are Point-to-Line, Line-to-Surface, Surface-to-Surface and Volume-to-Volume adjacency. As a result, all adjacencies were able to be executed for both datasets which consisted of two connected buildings and disjointed buildings. It was found that the ability of the 3D topological data structure to preserve topological primitives and build topological links supported the maintenance of connectivity information between buildings. The maintenance of connectivity information was also not limited to objects of the same dimension and could extend to connectivity between building elements in different dimensions.
Abstract. 3D city model is a representation of urban area in digital format that contains building and other information. The current approaches are using photogrammetry and laser scanning to develop 3D city model. However, these techniques are time consuming and quite costly. Besides that, laser scanning and photogrammetry need professional skills and expertise to handle hardware and tools. In this study, videogrammetry is proposed as a technique to develop 3D city model. This technique uses video frame sequences to generate point cloud. Videos are processed using EyesCloud3D by eCapture. EyesCloud3D allows user to upload raw data of video format to generate point clouds. There are five main phases in this study to generate 3D city model which are calibration, video recording, point cloud extraction, 3D modeling and 3D city model representation. In this study, 3D city model with Level of Detail 2 is produced. Simple query is performed from the database to retrieve the attributes of the 3D city model.
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