06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access Automation in laser scanning for cultural heritage applications(2005) Böhm, Jan; Haala, Norbert; Alshawabkeh, YahyaWithin the paper we present the current activities of the Institute for Photogrammetry in cultural heritage documentation in Jordan. In particular two sites, Petra and Jerash, were recorded using terrestrial laser scanning (TLS). We present the results and the current status of the recording. Experiences drawn from these projects have led us to investigate more automated approaches to TLS data processing. We detail two approaches within this work. The automation of georeferencing for TLS data is presented along with our approach for automated feature extraction.Item Open Access Integration of laser scanning and photogrammetry for heritage documentation(2006) Alshawabkeh, Yahya; Fritsch, Dieter (Prof. Dr.-Ing. habil.)The use of 3D laser scanner in documenting heritage sites has increased significantly over the past few years. This is mainly due to advances of such systems, which allow for the fast and reliable generation of millions of 3D points. Despite the considerable progress of these approaches, the highest possible degree in efficiency and flexibility of data collection will be possible, if other techniques are combined during data processing. Within the research the potential of combining terrestrial laser scanning and close range photogrammetry for the documentation of heritage sites is discussed. Besides improving the geometry of the model, the integration aims on supporting the visual quality of the linear features like edges and cracks in the historical scenes. Although the laser scanner gives very rich surface details, it does not provide sufficient data to construct outlines for all surface features of the scanned object, even though they are clearly defined in the reality. In our approach, information on edges and linear surface features is based on the analysis of the images. For this purpose an integrated segmentation process based on image data will support the ex-traction of object geometry information from the laser data. The approach applies image based semi-automated tech-niques in order to bridge gaps in the laser scanner data and add new details, which are required to build more realistic perception of the scene volume. Cultural heritage applications frequently require data collection by terrestrial laser scanning in very complex structural environments. Thus, compared to similar applications in industrial environments, this requires more complex process-ing to generate geometric models of sufficient realism. In addition to geometric data collection, texture mapping is particular important in the area of cultural heritage to have more complete documentation. Photo-realistic texturing can for example add information about the structure condition, which is not present in the 3D model such as decay of the material. Additionally, color image information is also indispensable for features like frescos and mosaics. In addi-tion to that, texture mapping considered as a requirement application for visualization and animation purposes. For this reason, some commercial 3D systems already provide model-registered color texture by capturing the RGB values of each LIDAR point using a camera already integrated in the system. However, these images frequently are not suffi-cient for high quality texturing, which is desired for documentation, since the ideal conditions for taking the images may not coincide with those for laser scanning In addition, laser scanning from many viewpoints, as it is required to capture complex structures, is still relatively time consuming. For outdoor applications these large time differences will result in varying light conditions and changing shadows, thus the recorded images will have different radiometric properties. Such problems may disturb the appearance of the resulting textured model. So it is therefore more useful to acquire geometry and texture by two independent processes and allow for an image collection at optimal position and time for texturing. This is especially true for the high requirements of realistic documentation of heritage sites. Thus, the thesis presents an approach for projective texture mapping from photographs onto triangulated surfaces from 3D laser scanning. By these means, the effort to generate photo-realistic models of complex shaped objects can be re-duced considerably. The images are collected from multiple viewpoints, which do not necessarily correspond to the viewpoints of LIDAR data collection. In order to handle the resulting problem of occlusions, the visibility of the model areas in the respective images has to be established. For this purpose, a new visible surface algorithm has been developed, the algorithm works in both image and object space and efficiently detects ambient, back-face and view frustum occlusions. Occluding polygons are labelled and separated with their connectivity to texture them recursively using the optimal of the available images until the final textured model is produced. After this visibility processing, colour values will be correctly assigned from the photograph to the visible polygons. In order to gain a high quality texture, lens distortion and colour corrections are applied during processing. The quality of the registration process, which aligns the laser scanner data with the imagery, is a crucial factor for the aspired combined processing. This registration can realized if correspondence coordinates are available in both sys-tems. Since the accurate detection and measurement of point correspondences can be difficult especially for the point clouds from laser scanning, straight lines are measured between the image and the laser data as corresponding ele-ments. The accuracy of the transformation depends on the accuracy with which the features have been extracted from the scans. For providing stable features of interest efficient segmentation algorithms have to be used to extract the features automatically for the co registration of data sets. In this thesis we have presented and discussed efficient edge detection algorithm that can detect the line features in both range and intensity images. In the proposed algorithm the distinguished points, which will comprise the edges, depend on the spatial analysis of the numerical description of the mean curvature values. The work was motivated by the fact that the optimality of edge detectors for range images has not been considered in the literature, some algorithms are limited to synthetic range images and will totally fail in the presence of noise, others which have been tested in real range images are complicated with large numbers of parame-ters. Compared to known methods in literature, the proposed algorithm exhibits the following features: computational efficiency, high accuracy in the localization of the edge points, easy to implement, and Image noise does not degener-ate its performance. Although the central task of the proposed edge detection algorithm is to reliable detect and locate edge points, a rich description of edge points give the ability to reliably detecting and characterizing the edge types as a crease and step edges, and then go further to classify the crease edges as concave or convex types. The algorithm was initially proposed for range image segmentation and has been extended to segment the intensity images with some improvements. The generality and robustness of the algorithm is illustrated on scene images with different available range sensors.