RRTrack: 動的シーンにおける頑健で復元可能な物体6D姿勢追跡
RRTrack: Robust and Recoverable Object 6D Pose Tracking for Dynamic Scenes
動的で遮蔽のあるシーンでの物体6D姿勢追跡を高速かつ頑健に行うため、2D-6D閉ループ追跡とDINOv2ベースのテンプレートマッチングによる再検出機能を備えたトラッカーRRTrackを提案した。
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著者: Junyue Li, Ye Zheng, Yifan Chen, Zhe Sun, Xuelong Li
分類: cs.CV, cs.RO
原文アブストラクト
Robust object 6D pose tracking is critical for robotic systems operating in dynamic and occluded scenes. Per-frame estimators are accurate but computationally expensive, while current trackers struggle with fast motion and complete occlusion due to their reliance on continuous visibility. To address these challenges, we present RRTrack, an efficient, recoverable object 6D pose tracker that enables robust tracking through fast motion and target disappearance--reappearance. RRTrack introduces a 2D--6D closed-loop tracking strategy that integrates memory-based video object segmentation (VOS) with 6D pose refinement. The 2D branch maintains target localization, and the 6D branch verifies geometric consistency before memory updates. In addition, a DINOv2-based dual-bank template matching module is developed to recover lost targets by jointly exploiting offline synthetic templates and online observation anchors while maintaining real-time efficiency. We also introduce a synthetic RGB-D benchmark comprising three robotic scenarios with fast motion and full occlusion. Experimental results on the synthetic benchmark demonstrate that RRTrack improves equal-subset mean ADD-S AR by 66.3\% and ADD-S AUC by 65.7\% over FoundationPose while achieving 55.2 FPS. Real-world experiments further validate the robustness of RRTrack under noisy sensing conditions. Project page: https://github.com/7kevin24/RRTrack
