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This paper presents a new method for reconstructing curves and surfaces from unstructured point clouds, allowing for noise in the data as well as inhomogeneous distribution of the point set. It is based on the observation that the curve/surface is located where locally the point cloud has highest density. This idea is pursued by a differential geometric analysis of a smoothed version of the density function. More precisely we detect ridges of this function and have to single out the relevant parts. An efficient implementation of this approach evaluates the differential geometric quantities on a regular grid, performs local analysis and finally recovers the curve/surface by an isoline extraction or a marching cubes algorithm respectively. Compared to existing surface reconstruction procedures, this approach works well for noisy data and for data with strongly varying sampling rate. Thus it can be applied successfully to reconstruct surface geometry from time-of-flight data, overlapping registered point clouds and point clouds obtained by feature tracking from video streams. Corresponding examples are presented to demonstrate the advantages of our method.