Categorization of Object Tracking Methods
Figure 1: The Categorization of Tracking Methods.
论文上下文
Paper title: Exploring Category-level Articulated Object Pose Tracking on SE(3) Manifolds Abstract: Articulated objects are prevalent in daily life and robotic manipulation tasks. However, compared to rigid objects, pose tracking for articulated objects remains an underexplored problem due to their inherent kinematic constraints. To address these challenges, this work proposes a novel point-pair-based pose tracking framework, termed \textbf{PPF-Tracker}. The proposed framework first performs quasi-canonicalization of point clouds in the SE(3) Lie group space, and then models articulated objects using Point Pair Features (PPF) to predict pose voting parameters by leveraging the invariance properties of SE(3). Finally, semantic information of joint axes is incorporated to impose unified kinematic constraints across all parts of the articulated object. PPF-Tracker is systematically evaluated on both synthetic datasets and real-world scenarios, demonstrating strong generalization across diverse and challenging environments. Experimental results highlight the effectiveness and robustness of PPF-Tracker in multi-frame pose tracking of articulated objects. We believe this work can foster advances in robotics, embodied intelligence, and augmented reality. Codes are available at https://github.com/mengxh20/PPFTracker. Passages referencing this figure: Figure 1: The Categorization of Tracking Methods. Traditional approaches often depend on dense predictions or frame-by-frame pose updates (Fig. 1 (a,b)) ( you2020multi ; wang20123 ) , which are computationally costly and unsuitable for real-time point cloud streams. To address the second challenge, we incorporate temporal priors from adjacent frames to guide pose prediction (Fig. 1 (c)).
