A large number of current mobile robots use 3D sensors as part of their sensor setup. Common 3D sensors, i.e., laser scanners or RGB-D cameras, emit a signal (laser light or infrared light for instance), and its reflection is recorded in order to estimate depth to a surface. The resulting set of measurement points is commonly referred to as 'point clouds'. In the first part of this dissertation an inherent problem of sensors that emit some light signal is addressed, namely that these signals can be reflected and/or refracted by transparent of highly specular surfaces, causing erroneous or missing measurements. A novel heuristic approach is introduced how such objects may nevertheless be identified and their size and shape reconstructed by fusing information from several viewpoints of the scene. In contrast to other existing approaches no prior knowledge about the objects is required nor is the shape of the reconstructed objects restricted to a limited set of geometric primitives. The thesis proceeds to illustrate problems caused by sensor noise and registration errors and introduces mechanisms to address these problems. Finally a quantitative comparison between equivalent directly measured objects, the reconstructions and "ground truth" is provided. The second part of the thesis addresses the problem of automatically determining the quality of the registration for a pair of point clouds. Although a different topic, these two problems are closely related, if modeled in the fashion of this thesis. After illustrating why the output parameters of a popular registration algorithm (ICP) are not suitable to deduce registration quality, several heuristic measures are developed that provide better insight. Experiments performed on different datasets were performed to showcase the applicability of the proposed measures in different scenarios.

Applicable image-based reconstruction of three-dimensional (3D) objects offers many interesting industrial as well as private use cases, such as augmented reality, reverse engineering, 3D printing and simulation tasks. Unfortunately, image-based 3D reconstruction is not yet applicable to these quite complex tasks, since the resulting 3D models are single, monolithic objects without any division into logical or functional subparts. This thesis aims at making image-based 3D reconstruction feasible such that captures of standard cameras can be used for creating functional 3D models. The research presented in the following does not focus on the fine-tuning of algorithms to achieve minor improvements, but evaluates the entire processing pipeline of image-based 3D reconstruction and tries to contribute at four critical points, where significant improvement can be achieved by advanced human-computer interaction: (i) As the starting point of any 3D reconstruction process, the object of interest (OOI) that should be reconstructed needs to be annotated. For this task, novel pixel-accurate OOI annotation as an interactive process is presented, and an appropriate software solution is released. (ii) To improve the interactive annotation process, traditional interface devices, like mouse and keyboard, are supplemented with human sensory data to achieve closer user interaction. (iii) In practice, a major obstacle is the so far missing standard for file formats for annotation, which leads to numerous proprietary solutions. Therefore, a uniform standard file format is implemented and used for prototyping the first gaze-improved computer vision algorithms. As a sideline of this research, analogies between the close interaction of humans and computer vision systems and 3D perception are identified and evaluated. (iv) Finally, to reduce the processing time of the underlying algorithms used for 3D reconstruction, the ability of artificial neural networks to reconstruct 3D models of unknown OOIs is investigated. Summarizing, the gained improvements show that applicable image-based 3D reconstruction is within reach but nowadays only feasible by supporting human-computer interaction. Two software solutions, one for visual video analytics and one for spare part reconstruction are implemented. In the future, automated 3D reconstruction that produces functional 3D models can be reached only when algorithms become capable of acquiring semantic knowledge. Until then, the world knowledge provided to the 3D reconstruction pipeline by human computer interaction is indispensable.