Generación automática de modelos 3D con escáneres y tecnologías inteligentes

The automatic generation of building information models with laser scanners
is an emergent research line in the reverse engineering field. The creation of this
kind of models has been made by hand during the last years, leading to suppose
a complex and tedious work. Therefore, the automation of this process is an
interesting challenge.
In this thesis, a work focused on the automatic reconstruction of inhabited
interiors is presented. Firstly, 3D point clouds, which are acquired from strategic
positions, are processed in order to identify and pose the structural components
of the scene. Then, a boundary representation (B-Rep) model is created. These
models contain the location and relationships of structural elements of inhabited
scenarios such as walls, ceilings, floors, columns, doors and windows.
The scene, enclosed by the calculated B-Rep model, is also composed of a
set of basic pieces of furniture. These “non-permanent” elements, which can be
relocated or removed in the scene, are also identified and positioned. Some authors
have developed different algorithms in order to localize objects in interior
environments. However, these processes (mainly based on Computer Vision) are
complex, computationally expensive and the results are unaccurate. In this dissertation,
a more flexible and novel solution to this problem is proposed, combining
laser scanners and radio-frequency identitication (RFID) technologies. The general
strategy consists of carrying out a selective and sequential segmentation of the
point cloud by means of different algorithms which depend on the information
that the RFID tags provide. These tags, attached to pieces of furniture, store
geometrical information of the objects, making the identification and positioning
of basic elements in the scene faster and easier.
This method has been tested in real scenes yielding promising results. An in
depth assessment has been performed, analyzing how reliably these elements can
be detected and how accurately they are modeled. Finally, we can conclude that
this proposal yields accurate 3D models which may be used for further purposes
related to the scene understanding.