Surface Reconstruction: Online Mosaicing and Modeling with Uncertainty

A burst of research has been made during the last decade on 3D Reconstruction and several interesting and well-behave algorithms have been developed. However, as scanning technologies improve their performance, reconstruction methods have to tackle new problems such as working with datasets of large dimension and building meshes almost in real-time. We pointed out a general need for formal analysis of the reconstruction problem and for methods which are able: (i) to elaborate huge and complex input datasets and to produce accurate results, (ii) to exploit all information provided by sensing devices, (iii) to transmit models quickly and accurately in order to visualize, search, and modify them using different devices (PDA, laptop, special devices, and so on). This PhD dissertation addresses the problem of reconstruction from two different points of view: Online Mosaicing and Modeling with uncertainty. Online Mosaicing: the Thesis presents a Data Analysis approach which on the fly, starting from multiple acoustic/optical range images, elaborates the acquired unknown object by mosaicing multiple single frame meshes. In the context of the European ARROV project, we developed a 3D reconstruction pipeline, which provides a 3D model of an underwater scene from a sequence of range data captured by an acoustic camera mounted on board a Remotely Operated Vehicle (ROV). Our approach works on line by building the 3D model while the range images arrive from ROV. The method combines the range images in a sequence by minimizing the workload of the rendering system. Modeling with uncertainty: The Thesis presents a general Surface Reconstruction framework which encapsulates the uncertainty of the sampled data, making no assumption on the shape of the surface to be reconstructed. Starting from the input points (either points clouds or multiple range images), we construct an Estimated Existence Function (EEF) that models 4 the space in which the desired surface could exist and, by the extraction of EEF critical points, we reconstructs the surface. The final goal is the development of a generic framework able to adapt the result to different kind of additional information coming from sensors, such as sampling conditions, normals, local curvature, and reliability of the data. 5 To Franco, the sun that drives out winter from my heart-6 Tell me and I will forget, Show me and I will remember, Let me do it and I will understand [Confucius] 7 Acknowledgments