3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.
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