This paper describes a mobile application that builds and updates a 3D model of an indoor environment, including walls, floor and openings, by a simple scan performed using a tablet equipped with a depth sensor. This algorithm is fully implemented on the device, does not require internet connection and runs in real-time, i.e., at five frames per second. This is made possible by taking advantage of recent AR frameworks, by assuming that the structure of the room is aligned on an Euclidean grid and by simply starting the scan in front of a wall. The wall detection is achieved in two steps. First, each incoming point cloud is segmented into planar wall candidates. Then, these planes are matched to the previously detected planes and labeled as ground, ceiling, wall, openings or noise depending on their geometric characteristics. Our evaluations show that the algorithm is able to measure a plane-to-plane distance with a mean error under 2 cm, leading to an accurate estimation of a room dimensions. By avoiding the generation of an intermediate 3D model, as a mesh, our algorithm allows a significant performance gain. The 3D model can be exported to a CAD software, in order to plan renovation works or to estimate energetic performances of the rooms. In the user experiments, a good usability score of 75 is obtained.