# Automatic Point-Cloud Registration for Quality Control in Building Works
## 1 Introduction  
The demand for 3D models of building interiors has grown in the recent years due to (1) the need to have as-built 3D models available for tasks related to planning and maintenance in buildings and (2) the proliferation of the building information modelling (BIM) standard [1; 2; 3; 4]. The most common techniques for the generation of 3D models have been photogrammetry [5; 6; 7] and laser scanning [8; 9]. Regarding the first, evolution has come with advances in computer vision and structure-from-motion techniques. These made possible the computation of the orientation and self-calibration parameters of a high number of images regardless the a priori knowledge of position and orientation. However, aspects such as presence of homogeneous surfaces (i.e., textureless), changes in illumination, the need to scale the model and the computational cost have been the main drawbacks when generating quality 3D models in indoor scenes with photogrammetry. With respect to the second technique, terrestrial laser scanners (TLS) allow the generation of high-precision 3D models [10; 11; 12; 13], but the acquisition requires meticulous planning, together with a high number of acquisition sets, and the application of an efficient registration technique to join all the point clouds acquired in the same point cloud, within a unique coordinate system. This issue has been studied several times in the last years and several solutions have been developed. Table 1 shows a comparison between different approaches attending to five different aspects: (i) the need of artificial targets, (ii) the need of RGB images, (iii) the number of point clouds that can be aligned with each method (i.e., only two point clouds or several point clouds), (iv) the minimum overlap between scans and v) the sensitivity tonoise. For example, [14] developed a fully automatic registration method of point clouds using special targets attached to the objects. The algorithm searches the specific templates of these targets using radiometric and geometric information (shape, size and planarity) and evaluates invariant parameters between each scan to calculate the registration. Although this method works, it depends on the placement of artificial targets in the scene. Placing targets is not always possible, and it requires previous works and detailed studies of the environment. Other studies have evaluated the registration using shape patterns between scans, such as planes [15]. This way, two point clouds are coarsely aligned using plane extraction to find tie points. This method is time-consuming and works only for pairs of point clouds, not being directly applicable to a set of scans. [16] implemented a registration method between scans based on linear and planar shapes to find homologous points and apply a coarse alignment. In [17], 2D images were used in combination with 3D models to improve point clouds registration. Two-dimensional images are used to add texture to the point clouds and to provide information about common features through their search with the SURF descriptor. With this, the matrix transformation is created and refined with different strategies such as RANSAC. This method reaches sufficient results only when the overlap between scans is good. To solve this problem, [18] presented a novel methodology to register outdoor and indoor point clouds using a two-step process: first, they extracted common features using the RGB information acquired by a digital camera and then they refined the alignment using iterative closest points (ICP) or planes matching techniques, that work properly even when the overlap between scans is poor. Both methods are robust and suitable for indoor and outdoor scenarios, but a hybrid calibrated system composed by an RGB camera and a laser scanner is mandatory. Moreover, both methods are based on the use of RGB images and textures, so it is not applicable for scans without that information. A fully image-based methodology was proposed by [19].