Real-time Background-aware 3D Textureless Object Pose Estimation
This work addresses efficiency in pose estimation for robotics or AR applications, but it is incremental as it builds on existing decision forest methods.
The paper tackles real-time 3D object pose estimation by introducing a modified fuzzy decision forest with a background rejector node to improve efficiency, achieving state-of-the-art speed while maintaining comparable accuracy.
In this work, we present a modified fuzzy decision forest for real-time 3D object pose estimation based on typical template representation. We employ an extra preemptive background rejector node in the decision forest framework to terminate the examination of background locations as early as possible, result in a significantly improvement on efficiency. Our approach is also scalable to large dataset since the tree structure naturally provides a logarithm time complexity to the number of objects. Finally we further reduce the validation stage with a fast breadth-first scheme. The results show that our approach outperform the state-of-the-arts on the efficiency while maintaining a comparable accuracy.