Reconstructing and Repairing Urban Models with Kinetic Data Structures. (Reconstruction et Correction de Modèles Urbains à l'Aide de Structures de Données Cinétiques)

Mulin Yu

Abstract: Compact and accurate digital 3D models of buildings are commonly used by practitioners for the visualization of existing or imaginary environments, the physical simulations or the fabrication of urban objects. Generating such ready-to-use models is however a difficult problem. When created by designers, 3D models usually contain geometric errors whose automatic correction is a scientific challenge. When created from data measurements, typically laser scans or multiview images, the accuracy and complexity of the models produced by existing reconstruction algorithms often do not reach the requirements of the practitioners. In this thesis, I address this problem by proposing two algorithms: one for repairing the geometric errors contained in urban-specific formats of 3D models, and one for reconstructing compact and accurate models from input point clouds generated from laser scanning or multiview stereo imagery. The key component of these algorithms relies upon a space-partitioning data structure able to decompose the space into polyhedral cells in a natural and efficient manner. This data structure is used to both correct geometric errors by reassembling the facets of defect-laden 3D models, and reconstruct concise 3D models from point clouds with a quality that approaches those generated by Computer-Aided-Design interactive tools.My first contribution is an algorithm to repair different types of urban models. Prior work, which traditionally relies on local analysis and heuristic-based geometric operations on mesh data structures, is typically tailored-made for specific 3D formats and urban objects. We propose a more general method to process different types of urban models without tedious parameter tuning. The key idea lies on the construction of a kinetic data structure that decomposes the 3D space into polyhedra by extending the facets of the imperfect input model. Such a data structure allows us to re-build all the relations between the facets in an efficient and robust manner. Once built, the cells of the polyhedral partition are regrouped by semantic classes to reconstruct the corrected output model. I demonstrate the robustness and efficiency of the algorithm on a variety of real-world defect-laden models and show its competitiveness with respect to traditional mesh repairing techniques from both Building Information Modeling (BIM) and Geographic Information Systems (GIS) data.My second contribution is a reconstruction algorithm inspired by the Kinetic Shape Reconstruction method, that improves the later in different ways. In particular, I propose a data fitting technique for detecting planar primitives from unorganized 3D point clouds. Departing from an initial configuration, the technique refines both the continuous plane parameters and the discrete assignment of input points to them by seeking high fidelity, high simplicity and high completeness. The solution is found by an exploration mechanism guided by a multi-objective energy function. The transitions within the large solution space are handled by five geometric operators that create, remove and modify primitives. I demonstrate its potential, not on buildings only, but on a variety of scenes, from organic shapes to man-made objects.