Active Control Points-based 6DoF Pose Tracking for Industrial Metal Objects
This provides a solution for real-time tracking in industrial applications, but it appears incremental as it builds on existing pose tracking methods with specific adaptations for metal objects.
The paper tackles the challenge of 6DoF pose tracking for industrial metal objects, which is difficult due to reflections, by proposing a method based on active control points that uses image control points to generate edge features for optimization, and it performs effectively in dataset evaluations and real-world tasks.
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.