In the last few years, notable progress has been made in the field of non-invasive diagnostic for the monitoring of heritage assets. In particular, multispectral imagery (more specifically thermal images will be addressed in this manuscript) allows investigations in the non-visible range of the electro-magnetic spectrum to be effectively carried out. Many researchers are currently exploring the possibilities related to the use of this kind of images in photogrammetric SfM-based processes to produce 2D and 3D value-added metric products, characterised by high level of detail and spatial resolution, including the information connected to the non-visible data. A data fusion-based strategy enables co-registering visible and thermal images in order to exploit the higher spatial resolution of the traditional true colour images. However, there are still many shortcomings to be addressed to properly and efficiently orient TIR (Thermal Infrared) images, connected (among other factors) to their low spatial resolution, or to the low contrast between adjacent materials characterised by similar emissivity. This paper proposes two different workflows to process thermal images using SfM algorithms, applied to three different case studies, each characterised by different characteristics and features (size, morphology, emissivity of the materials, etc.). The different pipelines are described and the obtained results are critically evaluated considering the metric accuracy, 3D geometric reconstruction and noise, completeness of the data and overall quality of the generated dense point cloud. Additionally, the effectiveness of the adopted strategies in connection with the peculiar features of the analysed case studies is also considered.