Robust and Accurate Detection of Mid-level Primitives for 3D Reconstruction in Man-Made Environments

The detection of geometric primitives such as points, lines and arcs is a fundamental step in computer vision techniques like image analysis,pattern recognition and 3D scene reconstruction. In this thesis, a framework is presented that provides robust and subpixel accurate detection of points, lines and arcs, and builds up a graph describing the topological relationships between the detected features. The focus is on the application in man-made environments. The detection method works directly on distorted perspective and fisheye images. The additional recognition of repetitive structures in images ensures the unambiguity of the features in their local environment. The detection method is integrated into a complete feature-based 3D reconstruction pipeline and a novel reconstruction method is presented thatuses the topological relationships of the features to create a highly abstract and semantically rich 3D model of the reconstructed scenes.

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