Papers › BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models

BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models

1 Aug 2021arXiv:2108.00516archive 2025-07-28

Bowen Wen, Kostas Bekris

Tracking the 6D pose of objects in video sequences is important for robot manipulation. Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching. This work proposes BundleTrack, a general framework for 6D pose tracking of novel objects, which does not depend upon 3D models, either at the instance or category-level. It leverages the complementary attributes of recent advances in deep learning for segmentation and robust feature extraction, as well as memory-augmented pose graph optimization for spatiotemporal consistency. This enables long-term, low-drift tracking under various challenging scenarios, including significant occlusions and object motions. Comprehensive experiments given two public benchmarks demonstrate that the proposed approach significantly outperforms state-of-art, category-level 6D tracking or dynamic SLAM methods. When compared against state-of-art methods that rely on an object instance CAD model, comparable performance is achieved, despite the proposed method's reduced information requirements. An efficient implementation in CUDA provides a real-time performance of 10Hz for the entire framework. Code is available at: https://github.com/wenbowen123/BundleTrack

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wenbowen123/BundleTrack officialmentioned in papermentioned on GitHubpytorchApache-2.0 report

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Tasks

3D Object Tracking 6D Pose Estimation 6D Pose Estimation using RGB 6D Pose Estimation using RGBD Object Tracking Pose Tracking Real-Time Visual Tracking Robot Manipulation Template Matching Video Object Tracking Visual Tracking

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
6D Pose Estimation using RGBD	REAL275	BundleTrack	Rerr	2.4	#10 of 11	Archive leaderboard	report
6D Pose Estimation using RGBD	REAL275	BundleTrack	Terr	2.1	#10 of 11	Archive leaderboard	report
6D Pose Estimation using RGBD	REAL275	BundleTrack	mAP 3DIou@25	99.9	#10 of 11	Archive leaderboard	report
6D Pose Estimation using RGBD	REAL275	BundleTrack	mAP 5, 5cm	87.4	#10 of 11	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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