Anisotropic point-based fusion

We propose a new real-time framework which efficiently reconstructs large-scale scenery by accumulating anisotropic point representations in combination with memory efficient representation of point attributes. The reduced memory footprint allows to store additional point properties that represent the accumulated anisotropic noise of the input range data in the reconstructed scene. We propose an efficient processing scheme for the extended and compressed point attributes that does not obstruct real-time reconstruction. Furthermore, we evaluate the positive impact of the anisotropy handling on the data accumulation and the 3D reconstruction quality.