The existing state of the art discloses different types of methods for modeling an object. Said methods are mainly classified into passive methods and active methods. In the area of active methods, sensors such as laser or structured light scanners or also Time-of-Flight type cameras are used. There are other possibilities such as projecting, with the aid of a video projector, a known pattern on an object and deducing the shape of the object by means of analyzing the deformation experienced by the pattern due to the shape of the object.
In the area of passive methods, most techniques exploit the geometric triangulation relating two or more views of the object of interest.
The present invention is in the field of the passive methods which include the following approaches of modeling objects based on views:                Structure from Motion, SfM, consisting of estimating the model of the scene in front of a camera in motion. However, the technique is only applicable to a set of multiple static cameras. Generally, an SfM algorithm establishes the match between the views of a set of points in the scene. By means of establishing this match, it is possible to triangulate the position of the points in the three dimensions of the space in front of one or several cameras. From this point, there are several possibilities for generating a model of an object. One possibility is using triangulation to calibrate the position of the camera throughout its motion or the position of each static camera. A dense model of the shape of the scene can be obtained, for example, by means of Shape from Stereo. Another possibility is assuming that the surface between any three points is locally flat. This model is therefore obtained by connecting points in groups of three by a triangle. The set of 3D triangles form a mesh representing the shape of the object. In this sense methods which reconstruct parts of the flat object are known from the state of the art. Firstly, matches between flat segments are established. Four points per segment or region are found and then a homography is induced. This homography allows establishing the epipolar geometry between the views. Finally, the set of segments can be positioned in 3D.        3D volumetric reconstruction. This approach encompasses from the least to the most precise modeling. For example, the box delimiting the real object would be a too coarse model. There are more precise models such as the Convex Hull (CH), the Visual Hull (VH) and the Photo Hull (PH). One of the most widespread volumetric models due to its good ratio between precision and low computational cost is the Visual Hull (VH). The Visual Hull is obtained by means of a method referred to as Shape-from-Silhouette (SfS). In a first phase, the Shape-from-Silhouette extracts the active entities of the scene (silhouettes of the object) by means of a set of cameras. The Visual Hull therefore corresponds with the volume inside the intersection of the cones going from the optical center of the cameras through the silhouettes in the optical planes of the cameras. The set of cameras must be intrinsically and extrinsically calibrated beforehand. The calibration can thus be obtained using the set of control points the coordinates of which are automatically known as a set of characteristic key points, as in the Structure from Motion approach.        Shape from Shading, “SfSh”, deals with recovering the shape from a gradual variation of the shading in the view. The idea behind Shape from Shading is that the color intensity can be described as a function of the surface, the shape, and the direction of the light source. Most SfSh algorithms assume that the direction of the light source is known.        
The passive methods described above have several drawbacks depending on the method used. In the case of methods based on Structure from Motion (SfM), the drawbacks arise from the objects without texture. In fact, in the absence of texture on the surface of the object, the resulting model is very coarse. In the case of very limited texture but with sufficient points for calibrating the set of cameras, the Shape from Stereo method can be used. However, the result of the previous method has the drawback that it is not capable of isolating the object from the objects forming the background or surrounding the object which is being modeled. In the particular case of the methods described above and which are based on finding four points of a segment and generating a homography, the entire calibration process depends on the possibility of establishing a match between the detected planes, which is not viable for objects without texture.
On the other hand, the Visual Hull obtained with a generic SfS method mainly depends on two aspects. Firstly, the positions of the cameras determined the efficiency of the SfS method. Another limitation of the applicability of this method is that the silhouettes are extracted by comparison with a known static background. This means that the object cannot be present in the scene when the background is captured. Consequently, this method is only valid for objects which can be easily obtained or introduced in the scene but not for modeling a part of a room, such as a wall or a fixed board.
It would therefore be desirable to find a method for generating a model of a flat object from views of the object which does not depend on the texture of the object to be modeled or on the consequent limitation involved in the correct calibration of the cameras, as well as to the capacity to move the object to be modeled with respect to the background or to the site in which the object is located.