Automatic 3D Modeling of Unstructured Scenes

Many computer vision and robotics applications call for accurate three-dimensional (3D) models of real-world objects. Current 3D modeling techniques require significant manual assistance or make assumptions about the scene characteristics or data collection procedure. In this thesis work, we propose to fully automate the 3D modeling process without resorting to these restrictive assumptions. Given a set of unordered range images and no additional a priori information about the scene, our system will generate an accurate 3D reconstruction. Specifically, it is not necessary to know the relative pose between viewpoints or to indicate which views contain over-lapping scene regions. Our proposed automatic modeling system selects pairs of views that are likely to match and attempts to register them. The results are verified for consistency, but some incorrect matches may be locally undetectable and some correct matches may be missed. One of several discrete optimization techniques is employed to combine these potentially faulty pair-wise matches into a network of views called the model graph. Incorrect pair-wise matches are detected by the incon-sistencies they produce elsewhere in the model graph, while missed matches are recovered b