Segmentation-driven 6D Object Pose Estimation
This addresses robust pose estimation for robotics and AR/VR applications in cluttered environments, representing a strong incremental improvement over existing methods.
The paper tackles the problem of 6D object pose estimation under large occlusions by introducing a segmentation-driven framework that aggregates local pose predictions from visible parts, outperforming state-of-the-art on Occluded-LINEMOD and YCB-Video datasets with real-time performance.
The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. In both cases, the object is treated as a global entity, and a single pose estimate is computed. As a consequence, the resulting techniques can be vulnerable to large occlusions. In this paper, we introduce a segmentation-driven 6D pose estimation framework where each visible part of the objects contributes a local pose prediction in the form of 2D keypoint locations. We then use a predicted measure of confidence to combine these pose candidates into a robust set of 3D-to-2D correspondences, from which a reliable pose estimate can be obtained. We outperform the state-of-the-art on the challenging Occluded-LINEMOD and YCB-Video datasets, which is evidence that our approach deals well with multiple poorly-textured objects occluding each other. Furthermore, it relies on a simple enough architecture to achieve real-time performance.