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3D modeling of building architecture from point-cloud scans is a rapidly advancing field. These models are used in augmented reality, navigation, and energy simulation applications. State-of-the-art scanning produces accurate point-clouds of building interiors containing hundreds of millions of points. Current surface reconstruction techniques either do not preserve sharp features common in a man-made structures, do not guarantee water tightness, or are not constructed in a scalable manner. This paper presents an approach that generates watertight triangulated surfaces from input point-clouds, preserving the sharp features common in buildings. The input point-cloud is converted into a voxelized representation, utilizing a memory-efficient data structure. The triangulation is produced by analyzing planar regions within the model. These regions are represented with an efficient number of elements, while still preserving triangle quality. This approach can be applied to data of arbitrary size to result in detailed models. We apply this technique to several data sets of building interiors and analyze the accuracy of the resulting surfaces with respect to the input point-clouds.