Xianhui Meng — accepted AI-conference papers

2026

DICArt: Advancing Category-level Articulated Object Pose Estimation in Discrete State-Spaces

Li Zhang, Mingyu Mei, Ailing Wang, Xianhui Meng, Yan Zhong, Xinyuan Song +4

CVPR 2026

Articulated object pose estimation is a core task in embodied AI and computer vision. Existing methods typically regress poses in a continuous space, but often struggle with 1) navigating a large, complex search space and 2) failing to incorporate intrinsic kinematic constraints. In this paper, we i

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2026

Exploring Category-level Articulated Object Pose Tracking on SE(3) Manifolds

Xianhui Meng, Yukang Huo, Li Zhang, Liu Liu, Haonan Jiang, Yan Zhong +3

AAAI 2026technical

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-b

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