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In this article we present an algorithm for the fusion of depth images derived by dense image matching (DIM). One key idea of ouralgorithm is to generate a 2D triangulation for each available depth map in the image sequence using a restricted quadtrees (RQT). Onthe one hand this guarantees <i>matching triangulations</i>, on the other hand this creates the possibility to reduce points in the noise rangenot contributing to the geometry in a controlled manner. By vertex decimation computational efforts in subsequent processing stepsare eased. In order to reduce IO overhead, the algorithm is designed in an iterative way: an initial triangulation is lifted to 3D spaceand, if pixel footprints are comparable, updated using depths of the subsequent map in the sequence. Previously not observed surfaceregions or surface patches observed only with adverse precision are removed from the existing model and updated by more appropriatetriangulations. Thereby differences in scale across depth maps are handled which is particularly important to preserve details andobtain surfaces with the best reconstruction geometry. To remove outliers visibility constraints are forced. The input is overlappingdepth images and their poses in space, the output are point coordinates representing the surface, their respective normals and to somedegree spatial neighbourhood information of points represented as a non-watertight mesh. The performance of the algorithm will beevaluated on a close range and a oblique aerial dataset

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Generating Oriented Pointsets From Redundant Depth Maps Using Restricted Quadtrees