  Tracking objects in 3D space and predicting their 6DoF pose is an essential
task in computer vision. State-of-the-art approaches often rely on object
texture to tackle this problem. However, while they achieve impressive results,
many objects do not contain sufficient texture, violating the main underlying
assumption. In the following, we thus propose ICG, a novel probabilistic
tracker that fuses region and depth information and only requires the object
geometry. Our method deploys correspondence lines and points to iteratively
refine the pose. We also implement robust occlusion handling to improve
performance in real-world settings. Experiments on the YCB-Video, OPT, and Choi
datasets demonstrate that, even for textured objects, our approach outperforms
the current state of the art with respect to accuracy and robustness. At the
same time, ICG shows fast convergence and outstanding efficiency, requiring
only 1.3 ms per frame on a single CPU core. Finally, we analyze the influence
of individual components and discuss our performance compared to deep
learning-based methods. The source code of our tracker is publicly available.

Computer Vision and Pattern Recognition