Thermal imaging has become a valuable tool in various fields for remote sensing and can provide relevant information to perform object recognition or classification. In this paper, we present an automated method to obtain a 3D model fusing data from a visible and a thermal camera. The RGB and thermal point clouds are generated independently by structure from motion. The registration process includes a normalization of the point cloud scale, a global registration based on calibration data and the output of the structure from motion, and a fine registration employing a variant of the Iterative Closest Point optimization. Experimental results demonstrate the accuracy and robustness of the overall process.
SUMMARYIn this paper, we propose a method to generate a threedimensional (3D) thermal map and RGB + thermal (RGB-T) images of a scene from thermal-infrared and RGB images. The scene images are acquired by moving both a RGB camera and an thermal-infrared camera mounted on a stereo rig. Before capturing the scene with those cameras, we estimate their respective intrinsic parameters and their relative pose. Then, we reconstruct the 3D structures of the scene by using Direct Sparse Odometry (DSO) using the RGB images. In order to superimpose thermal information onto each point generated from DSO, we propose a method for estimating the scale of the point cloud corresponding to the extrinsic parameters between both cameras by matching depth images recovered from the RGB camera and the thermal-infrared camera based on mutual information. We also generate RGB-T images using the 3D structure of the scene and Delaunay triangulation. We do not rely on depth cameras and, therefore, our technique is not limited to scenes within the measurement range of the depth cameras. To demonstrate this technique, we generate 3D thermal maps and RGB-T images for both indoor and outdoor scenes.
This paper presents a pipeline for stereo visual odometry using cameras with different fields of view. It gives a proof of concept about how a constraint on the respective field of view of each camera can lead to both an accurate 3D reconstruction and a robust pose estimation. Indeed, when considering a fixed resolution, a narrow field of view has a higher angular resolution and can preserve image texture details. On the other hand, a wide field of view allows to track features over longer periods since the overlap between two successive frames is more substantial. We propose a semi-independent stereo system where each camera performs individually temporal multi-view optimization but their initial parameters are still jointly optimized in an iterative framework. Furthermore, the concept of lead and follow camera is introduced to adaptively propagate information between the cameras. We evaluate the method qualitatively on two indoor datasets, and quantitatively on a synthetic dataset to allow the comparison across different fields of view.
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