Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
This addresses the challenge of tracking textureless objects in computer vision, which is crucial for applications like robotics and AR, but it appears incremental as it builds on existing tracking methods by handling texture limitations.
The paper tackles the problem of 3D tracking and 6DoF pose estimation for textureless objects by proposing ICG, a probabilistic tracker that fuses region and depth information based on object geometry, achieving state-of-the-art accuracy and robustness with 1.3 ms per frame on a single CPU core.
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.