PoseStreamer: A Multi-modal Framework for 3D Tracking of Unseen Moving Objects

• URL: http://arxiv.org/abs/2512.22979v3
• Date: Fri, 02 Jan 2026 12:58:07 GMT
• Title: PoseStreamer: A Multi-modal Framework for 3D Tracking of Unseen Moving Objects
• Authors: Huiming Yang, Linglin Liao, Fei Ding, Sibo Wang, Zijian Zeng,
• Abstract summary: PoseStreamer is a robust multi-modal 6DoF pose estimation framework for high-speed moving scenarios.<n>MoCapCube6D is a novel multi-modal dataset constructed to benchmark performance under rapid motion.
• Score: 4.1334804706669095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Six degree of freedom (6DoF) pose estimation for novel objects is a critical task in computer vision, yet it faces significant challenges in high-speed and low-light scenarios where standard RGB cameras suffer from motion blur. While event cameras offer a promising solution due to their high temporal resolution, current 6DoF pose estimation methods typically yield suboptimal performance in high-speed object moving scenarios. To address this gap, we propose PoseStreamer, a robust multi-modal 6DoF pose estimation framework designed specifically on high-speed moving scenarios. Our approach integrates three core components: an Adaptive Pose Memory Queue that utilizes historical orientation cues for temporal consistency, an Object-centric 2D Tracker that provides strong 2D priors to boost 3D center recall, and a Ray Pose Filter for geometric refinement along camera rays. Furthermore, we introduce MoCapCube6D, a novel multi-modal dataset constructed to benchmark performance under rapid motion. Extensive experiments demonstrate that PoseStreamer not only achieves superior accuracy in high-speed moving scenarios, but also exhibits strong generalizability as a template-free framework for unseen moving objects.

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