The recent advances in 3D laser range scanning have led to significant improvements in capturing and modeling 3D envi- ronments, allowing the creation of highly expressive and semantically rich 3D models from indoor environments, generally known as building information models. Despite the capabilities of state-of-the-art methods to generate faithful architectural 3D building models, the majority of them rely explicitly on the prior knowledge of scanner positions in order to reconstruct them successfully. However, in real-world applications, this metadata information gets typically lost after the point cloud registration, which means that none of these methods could work in practice and the creation of their building models would be impossible. Therefore, we present a novel pipeline that allows to automatically and accurately reconstruct the original scanner positions under very challenging conditions, without requiring any prior knowledge about the environment or the dataset. Being independent from laser range scanner manufacturers, it can be applied to almost every real-world LiDAR appli- cation. Our method exploits only information derived from the raw point data and is applicable to all scientific and industrial applications, where the original scan positions typically get lost after registration by the proprietary software provided by the scanner manufacturers. We demonstrate the validity of our approach by evaluating it on several real-world and synthetic indoor environments.