发表机构
College of Computer Science and Technology, Zhejiang University; Institute of Artificial Intelligence (TeleAI), China Telecom; College of Future Information Technology, Fudan University（浙江大学计算机科学与技术学院; 中国电信人工智能研究院; 复旦大学未来信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对动态场景中物体6D姿态跟踪难题，提出RRTrack。它采用2D-6D闭环跟踪策略，集成记忆视频对象分割与6D姿态细化，还有双库模板匹配模块。在合成基准和真实世界实验中验证，相比FoundationPose有显著提升，兼具高效与鲁棒性。
AI 中文摘要
鲁棒的物体6D姿态跟踪对于在动态和遮挡场景中运行的机器人系统至关重要。逐帧估计器准确但计算成本高，当前跟踪器因依赖连续可见性而难以应对快速运动和完全遮挡。为应对这些挑战，我们提出了RRTrack，一种高效、可恢复的物体6D姿态跟踪器，能通过快速运动和目标消失-重现实现鲁棒跟踪。它引入了2D-6D闭环跟踪策略，集成基于记忆的视频对象分割（VOS）与6D姿态细化。2D分支维持目标定位，6D分支在记忆更新前验证几何一致性。还开发了基于DINOv2的双库模板匹配模块来恢复丢失目标。我们还引入了包含三个具有快速运动和完全遮挡的机器人场景的合成RGB-D基准。实验结果表明，RRTrack在合成基准上比FoundationPose的等子集平均ADD-S AR提高了66.3%，ADD-S AUC提高了65.7%，同时实现了55.2 FPS。真实世界实验进一步验证了RRTrack在噪声传感条件下的鲁棒性。
英文摘要
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
Comments6D Pose Tracking, 10 pages, real-world experiments, training-free
