用合成数据提升单目相机在动态场景中的物体6D姿态追踪精度
OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
- 基于概率建模的不确定性感知关键点优化网络
- 在真实数据集上优于现有基线方法，显著提升追踪精度
- 适合做视觉追踪、机器人抓取等动态环境应用研究
为应对单目RGB输入下动态环境中短时物体6D姿态追踪的挑战，我们构建了一个大规模合成数据集OmniPose6D，以模拟真实世界的多样性。同时提出一个基准评估框架，用于全面比较姿态追踪算法。我们设计了一种包含不确定性感知关键点精炼网络的追踪流水线，采用概率建模优化姿态估计。对比实验表明，该方法在真实数据集上的表现优于现有基线，验证了合成数据集和精炼技术在复杂动态场景中提升追踪精度的有效性。本工作为复杂场景下物体姿态追踪方法的研发与评估树立了新标准。
原文摘要 · Abstract (English)
To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We additionally present a benchmarking framework for a comprehensive comparison of pose tracking algorithms. We propose a pipeline featuring an uncertainty-aware keypoint refinement network, employing probabilistic modeling to refine pose estimation. Comparative evaluations demonstrate that our approach achieves performance superior to existing baselines on real datasets, underscoring the effectiveness of our synthetic dataset and refinement technique in enhancing tracking precision in dynamic contexts. Our contributions set a new precedent for the development and assessment of object pose tracking methodologies in complex scenes.
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