解决快速移动相机与物体的6D姿态跟踪难题，无需重训练。
DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects
- 融合视觉惯性里程计与深度信息，动态补偿追踪区域偏移。
- 基于VIO引导的卡尔曼滤波生成候选姿态，实现高精度闭环追踪。
- 适用于高速运动场景，适合机器人、AR/VR等实时系统使用。
我们提出DynamicPose，一种无需重训练的6D姿态追踪框架，显著提升快速移动相机与物体场景下的追踪鲁棒性。以往方法主要适用于静态或准静态场景，当相机和物体同时高速运动时性能急剧下降。为此，我们设计三个协同组件：(1) 视觉惯性里程计（VIO）补偿由相机运动引起的感兴趣区域（ROI）偏移；(2) 深度感知2D追踪器修正因大物体平移导致的ROI偏差；(3) 基于VIO引导的卡尔曼滤波预测物体旋转，生成多个候选姿态，并通过分层精炼获得最终姿态。6D姿态结果反向指导后续2D追踪与卡尔曼滤波更新，形成闭环系统，确保姿态初始化准确与追踪精确。仿真与真实世界实验验证了方法有效性，实现了对快速运动相机与物体的实时、鲁棒6D姿态追踪。
原文摘要 · Abstract (English)
We present DynamicPose, a retraining-free 6D pose tracking framework that improves tracking robustness in fast-moving camera and object scenarios. Previous work is mainly applicable to static or quasi-static scenes, and its performance significantly deteriorates when both the object and the camera move rapidly. To overcome these challenges, we propose three synergistic components: (1) A visual-inertial odometry compensates for the shift in the Region of Interest (ROI) caused by camera motion; (2) A depth-informed 2D tracker corrects ROI deviations caused by large object translation; (3) A VIO-guided Kalman filter predicts object rotation, generates multiple candidate poses, and then obtains the final pose by hierarchical refinement. The 6D pose tracking results guide subsequent 2D tracking and Kalman filter updates, forming a closed-loop system that ensures accurate pose initialization and precise pose tracking. Simulation and real-world experiments demonstrate the effectiveness of our method, achieving real-time and robust 6D pose tracking for fast-moving cameras and objects.
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。