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The automatic analysis of 3D point clouds has become a crucial task in photogrammetry, remote sensing and computer vision. Whereasmodern range cameras simultaneously provide both range and intensity images with high frame rates, other devices can be used toobtain further information which could be quite valuable for tasks such as object detection or scene interpretation. In particular thermalinformation offers many advantages, since people can easily be detected as heat sources in typical indoor or outdoor environmentsand, furthermore, a variety of concealed objects such as heating pipes as well as structural properties such as defects in isolation maybe observed. In this paper, we focus on thermal 3D mapping which allows to observe the evolution of a dynamic 3D scene overtime. We present a fully automatic methodology consisting of four successive steps: (i) a radiometric correction, (ii) a geometriccalibration, (iii) a robust approach for detecting reliable feature correspondences and (iv) a co-registration of 3D point cloud data andthermal information via a RANSAC-based EPnP scheme. For an indoor scene, we demonstrate that our methodology outperformsother recent approaches in terms of both accuracy and applicability. We additionally show that efficient straightforward techniquesallow a categorization according to background, people, passive scene manipulation and active scene manipulation

Thermal 3D mapping for object detection in dynamic scenes