  Estimating the 3D pose of an object is a challenging task that can be
considered within augmented reality or robotic applications. In this paper, we
propose a novel approach to perform 6 DoF object pose estimation from a single
RGB-D image. We adopt a hybrid pipeline in two stages: data-driven and
geometric respectively. The data-driven step consists of a classification CNN
to estimate the object 2D location in the image from local patches, followed by
a regression CNN trained to predict the 3D location of a set of keypoints in
the camera coordinate system. To extract the pose information, the geometric
step consists in aligning the 3D points in the camera coordinate system with
the corresponding 3D points in world coordinate system by minimizing a
registration error, thus computing the pose. Our experiments on the standard
dataset LineMod show that our approach is more robust and accurate than
state-of-the-art methods. The approach is also validated to achieve a 6 DoF
positioning task by visual servoing.
