In this thesis a new algorithm for the reconstruction of surfaces from three-dimensional point clouds is presented. Its particular features are the reconstruction of open surfaces with boundaries, data sets with variable density, and the treatment of sharp edges, that is, locations of infinite curvature. We give formal arguments which explain why the algorithm works well. They consist of a definition of 'reconstruction', and the demonstration of existence of sampling sets for which the algorithm is successful. This mathematical analysis focuses on compact surfaces of limited curvature without boundary. Further contributions are the application of the surface reconstruction algorithm for interactive shape design and a smoothing procedure for noise elimination in point clouds. Additionally, the algorithm can be easily applied for locally-restricted reconstruction if only a subset of the data set has to be considered for reconstruction.