Many approaches have been proposed for human pose estimation in single and multi-view RGB images. However, some environments, such as the operating room, are still very challenging for state-of-the-art RGB methods. In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features. The proposed method permits the joint detection and estimation of the poses without knowing a priori the number of persons present in the scene. We evaluate this approach on a novel multi-view RGB-D dataset acquired during live surgeries and annotated with ground truth 3D poses.

In this paper, we address the problem of precisely recovering the 3D (three-dimensional) pose of 3D shape fiducials from images obtained by means of an uncalibrated computed tomography (CT) imaging device. The main goal in this work is to model and estimate the geometric transformation relating line fiducials to their projections in cross-sectional images. To do so, we propose techniques, which solve the points to lines correspondence using closed-form and numerical algorithms. A geometric transformation with eight degrees of freedom (rotation, translation and anisotropic scaling) is used to model both a rigid-body transformation and a scaling transformation accounting for CT scan intrinsic parameters. Furthermore, an estimation of error bounds in space is given when image data are affected by noise. Real experiments show that the proposed method provides good results on a set of CT images from many viewpoints.

With recent advancements of technology, radiofrequency ablation has become one of the most used techniques to treat liver tumors. But radiologists still have to face the difficulty of planning their treatment while only relying on 2D slices acquired from CT-scan. We present a tool called RF-Sim, being part of a complete 3D reconstruction and visualization project, and including both a realistic radiofrequency ablation simulator for training and rehearsal, and an automatic treatment planner taking into account tumor's environment. They help radiologists to have a better visualization of patients' anatomic structures and pathologies, and allow them to easily find an adequate treatment. They run on a common laptop and can be used in the operating room.