We propose a novel 3D object reconstruction framework that is able to fully capture the accurate coloured geometry of an object using an RGB-D sensor. Building on visual odometry for trajectory estimation, we perform pose graph optimisation on collected keyframes and reconstruct the scan variationally via coloured signed distance fields. To capture the full geometry, we conduct multiple scans while changing the object's pose. After collecting all coloured fields, we perform an automated dense registration over all collected scans to create one coherent model. We show on eight reconstructed real-life objects that the proposed pipeline outperforms the state-of-the-art in visual quality as well as geometrical fidelity.     «

We propose a novel 3D object reconstruction framework that is able to fully capture the accurate coloured geometry of an object using an RGB-D sensor. Building on visual odometry for trajectory estimation, we perform pose graph optimisation on collected keyframes and reconstruct the scan variationally via coloured signed distance fields. To capture the full geometry, we conduct multiple scans while changing the object's pose. After collecting all coloured fields, we perform an automated dense re...     »