For decades scene reconstruction from multiple images is a topic in computer vision and photogrammetry communities. Typical applications require very precise reconstructions and are not bound to a limited computation time. Techniques for these applications are based on complete sets of images to compute the scene geometry. They require a huge amount of resources and computation time before delivering results for visualization or further processing.#R##N##R##N#In the application of disaster management these approaches are not an option since the reconstructed data has to be available as soon as possible. Especially, when it comes to Miniature Unmanned Aerial Vehicles MUAVs sending aerial images to a ground station wirelessly while flying, operators can use the 3D data to explore the virtual world and to control the MUAVs.#R##N##R##N#In this paper an incremental approach for dense reconstructions from sparse datasets is presented. Instead of focussing on complete datasets and delivering results at the end of the computation process, our incremental approach delivers reasonable results while computing, for instance, to quickly visualize the virtual world or to create obstacle maps.
['Jan Roters', 'Xiaoyi Jiang']
Incremental Dense Reconstruction from Sparse 3D Points with an Integrated Level-of-Detail Concept