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Growing Surface Structures: an Iterative Refinement Surface Reconstruction Approach Deduced from Artificial Neural Networks

This thesis presents a high quality surface reconstruction approach based on an iterative refinement strategy deduced from artificial neural networks. The presented approach is composed of three novel developments extending the capabilities as well as the concept of the growing cell structures algorithm. An introduction to the associated fields of research is given and the problem of surface reconstruction from unorganized points is discussed in detail. A state of the art overview of all major surface reconstruction methods and their performance is presented. A detailed introduction to the development stages of the growing cell structures algorithm is given, which enables localizing algorithm properties for possible modification. The distinctions between the growing cell structures and the growing neural gas algorithm are discussed. The presented filter chain concept enables easy editability of the algorithm behavior and adds additional flexibility. The twist solving method efficiently and reliably fixes inconsistently oriented surface areas within the reconstruction process. The potential of the novel data structures used to solve this problem for other computer graphics applications is demonstrated. The growing cell structures algorithm concept is redesigned to incorporate an explicit surface representation into its learning scheme. This introduces novel possibilities to direct the algorithm’s approximation behavior. The final algorithm, composed of the combined presented developments, is evaluated in comparison to classical reconstruction algorithms as well as in reconstructing extremely challenging point clouds. The algorithm proves to be an efficient all-round high quality approach. Finally, the presented results and their implications for future work are discussed.