Online Surface Reconstruction From Unorganized Point Clouds With Integrated Texture Mapping
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Digital representations of the real world are becoming more and more important for different application domains. Individual objects, excavation sites or even complete cities can be digitized with today’s technology so that they can, for instance, be preserved as digital cultural heritage, be used as a basis for map creation, or be integrated into virtual environments for mission planning during emergency or disaster response tasks. Robust and efficient surface reconstruction algorithms are inevitable for these applications. Surface-reconstructing growing neural gas (sgng) presented in this dissertation constitutes an artificial neural network that takes a set of sample points lying on an object’s surface as an input and iteratively constructs a triangle mesh representing the original object’s surface. It starts with an initial approximation that gets continuously refined. At any time during execution, sgng instantly incorporates any modifications of the input data into the reconstruction. If images are available that are registered to the input points, sgng assigns suitable textures to the constructed triangles. The number of noticeable occlusion artifacts is reduced to a minimum by learning the required visibility information from the input data. Sgng is based on a family of closely related artificial neural networks. These are presented in detail and illustrated by pseudocode and examples. Sgng is derived according to a careful analysis of these prior approaches. Results of an extensive evaluation indicate that sgng improves significantly upon its predecessors and that it can compete with other state-of-the-art reconstruction algorithms.