通过主动控制点实现金属物体6自由度姿态实时跟踪
Active Control Points-based 6DoF Pose Tracking for Industrial Metal Objects
- 用图像控制点生成边缘特征主动优化姿态
- 在真实环境与数据集上均实现稳定高精度跟踪
- 适合工业场景中金属表面反光导致的跟踪难题
视觉姿态跟踪在工业场景中日益重要，但金属物体因反光特性导致姿态追踪仍具挑战。为此，我们提出一种基于主动控制点的6DoF姿态追踪方法。该方法不依赖基于姿态的渲染，而是利用图像控制点生成边缘特征并作为优化变量主动参与优化，并引入最优控制点回归以提升鲁棒性。所提方法在数据集评估和真实世界任务中均表现有效，为工业金属物体的实时姿态跟踪提供了可行方案。代码已公开：https://github.com/tomatoma00/ACPTracking。
原文摘要 · Abstract (English)
Visual pose tracking is playing an increasingly vital role in industrial contexts in recent years. However, the pose tracking for industrial metal objects remains a challenging task especially in the real world-environments, due to the reflection characteristic of metal objects. To address this issue, we propose a novel 6DoF pose tracking method based on active control points. The method uses image control points to generate edge feature for optimization actively instead of 6DoF pose-based rendering, and serve them as optimization variables. We also introduce an optimal control point regression method to improve robustness. The proposed tracking method performs effectively in both dataset evaluation and real world tasks, providing a viable solution for real-time tracking of industrial metal objects. Our source code is made publicly available at: https://github.com/tomatoma00/ACPTracking.
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