During an emergency inside large buildings such as hospitals and shopping malls, the availability of up-to-date information is critical. One common source of information is the 2D layout of buildings and emergency exits. For most buildings, this information is represented as tangled floor plans, which in most cases are outdated. One solution to update the data of buildings after each renovation is to recreate 3D models of buildings in a quick and automatic approach. These 3D models provide proactively crucial building information in a digital format for first responders to be used in emergency cases. Thanks to advances in remote sensing, laser scanners can be used to generate an accurate spatial representation of buildings quickly. However, such devices provide point clouds, which are unstructured data. In this paper, we introduce a complete workflow that allows to generate 3D models from point clouds of buildings and extract fine-grained indoor navigation networks from those models, to support advanced path planning for disaster management and navigation of different types of agents. The process extracts structural elements of buildings such as walls, slabs, ceiling and openings, and reconstruct their volumetric shapes. Additionally, the furnishing elements in the input point clouds are identified and reconstructed as the obstacles. Stairs are also reconstructed to allow multistory navigation path planning. Our algorithm is fully 3D and can handle vertical and slanted structures. We test it on several real datasets, compared it to the state-of-the-art approaches and provide a process to check the consistency of the reconstruction, which allows in return to further improve its result.