been properly dealt with in the current literature, and it is, therefore, possible to conclude that the few works focused on obtaining thermal clouds in buildings provide apparent temperatures. The difference between the apparent temperature and the real temperature of _objects_ in the construction field (i.e., walls, floors, ceilings, doors and windows) is conditioned by multiple factors and may be significant. In our tests, the specific original temperature values provided by the thermal camera have been corrected by up to 1.5 \({}^{\circ}\)C (with an average of 0.47 \({}^{\circ}\)C) from the original data captured by the thermal camera. This paper proposes an automatic temperature correction approach that covers the most important effects in our framework. We specifically address variations owing to the internal effects of the IR camera, which lead to an uncontrolled variability in temperature, the calculation of the real emissivity of the _objects_ and the reflected radiation effect. The experimentation carried out in order to test the method shows that, particularly in the case of the first effect, and to a lesser extent in that of the remaining two, the temperature correction is necessary. This is particularly the case if the eventual objective is to calculate quantitative measures of energy parameters, such as transmittance and others. Several experimental tests have been presented using 3D thermal point clouds taken from a 3D thermal digitizing platform. This system provides dense thermal point clouds that facilitate more reliable average temperature values than mere point temperature records. All this has allowed us, in a first step, to undertake thermal frame filtering procedures, thus improving the quality of the measurement provided by the thermal camera. Furthermore, the dense information contained in the data clouds obtained has allowed us to carry out image processing techniques on the thermal orthoimages, detecting and delimiting objects on the walls in the scene. These techniques were then used to carry out a second correction. As shown at the end of the paper, the inclusion of an application regarding the empirical calculation of the transmittance of several existing objects on a wall could be extended to other energy parameters in which a reliable temperature value is required and that is obtained from infrared cameras. In the case study described, it will be noted that there is an improvement in the values obtained after making the corrections presented. ## 8 Limitations and Future Work

This paper proposes an automatic temperature correction approach that requires very robust algorithms in the stages concerning the detection of aluminium and electric tapes. This image processing stage could fail in the case of textured and inhabited indoor buildings. Another disadvantage is that, owing to the fact that the thermal camera variability issue is solved by repeating thermal images from the same viewpoint and then detecting and removing the outlier images, the data-collection stage takes quite a long time, and a larger volume of data is obtained when compared to single sessions. This work will be improved in the coming months. In the first place, we aim to extend the experimentation carried out with the method for scenes with more extreme temperature ranges, which will be both low (below 5 \({}^{\circ}\)C) and high (above 30 \({}^{\circ}\)C), and the temperature correction is expected to be much higher. Moreover, we intend to attain a higher degree of automation throughout the process, especially as regards the creation of thermal orthoimages of walls and the recognition and assignment of the average temperatures of the objects detected. The future developments of this research will lead us to provide a more precise thermal characterisation of the indoors of buildings by using automatic hardware and software tools. On the one hand, the use of robotics platforms will allow us to accomplish thermal scanning sessions without human interaction, thus reducing time and gaining accuracy.