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Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects

10 Mar 2022CVPR 2022 1arXiv:2203.05334archive 2025-07-28

Manuel Stoiber, Martin Sundermeyer, Rudolph Triebel

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.

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dlr-rm/3dobjecttracking officialmentioned in paper report

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Tasks

3D Object Tracking6D Pose Estimation6D Pose Estimation using RGBDObject Tracking

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
6D Pose Estimation OPT ICG AUC 16.54 #2 of 2 Archive leaderboard report
6D Pose Estimation YCB-Video ICG ADDS AUC 96.5 #3 of 10 Archive leaderboard report

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