The past decade has seen a radical evolution in 3D surface acquisition and processing which has been driven by two general directions: designing ever higher quality digital acquisition devices on one hand, and designing low cost acquisition devices on the other hand. This evolution is similar to the one witnessed for digital cameras: on the one side ever better reflex cameras and on the other side cheap cameras integrated into mobile phones or computers. This development calls for a variety of tools able to deal with this varying quality to generate the highest resolution possible out of low quality scans and to process high accuracy surfaces. This project proposes to tackle this problem by developing efficient and scalable methods taking advantage of an intrinsic property of surfaces : their natural self-similarity. Indeed, most surfaces, be it from a fine-art artefact or a mechanical object, are characterized by a strong self-similarity. This property stems from the natural structures of objects but also from the fabrication processes: regularity of the sculpting technique, or machine tool. In the field of surface reconstruction from point clouds, existing approaches generally focus on reconstructing smooth closed surfaces. When the size of the data increases, global methods fail to recover both the shape geometry and the local details. We propose to explore new local approaches, the only ones well suited for large and precise data. The key idea of the project is to avoid processing the point cloud as a global shape, as is usually done in the Geometry Processing literature. Instead, our proposed approach takes into account a smaller scale : the points and their neighborhoods (patches). Analyzing surfaces at such a local scale will permit to reveal surfaces self-similarity, which is the core of our project.