提出可恢复的6D位姿追踪方法，解决动态场景中目标快速运动与完全遮挡问题。
RRTrack: Robust and Recoverable Object 6D Pose Tracking for Dynamic Scenes
- 2D-6D闭环追踪结合记忆分割与位姿优化，提升稳定性。
- 在合成数据集上相比FoundationPose提升ADD-S准确率66.3%，达55.2帧/秒。
- 适用于机器人视觉、自动驾驶等复杂动态场景下的高鲁棒性位姿追踪。
在动态和遮挡场景中，稳健的物体6D位姿追踪对机器人系统至关重要。单帧估计算法虽准确但计算开销大，而现有追踪器因依赖持续可见性，在快速运动和完全遮挡下表现不佳。为此，我们提出RRTrack，一种高效且具备恢复能力的6D位姿追踪方法，可在目标快速运动及消失-重现身时保持追踪。该方法采用2D-6D闭环追踪策略，融合基于记忆的视频目标分割（VOS）与6D位姿精修。2D分支负责目标定位，6D分支在更新记忆前验证几何一致性。此外，设计了基于DINOv2的双库模板匹配模块，联合利用离线合成模板与在线观测锚点实现丢失目标的恢复，同时保持实时效率。我们还构建了一个包含三个机器人场景的合成RGB-D基准，涵盖快速运动与完全遮挡。在该基准上的实验表明，与FoundationPose相比，RRTrack在等子集平均ADD-S准确率上提升66.3%，在ADD-S AUC上提升65.7%，并达到55.2 FPS。真实世界实验进一步验证了其在噪声传感条件下的鲁棒性。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。