论文标题
带有混合非本地优化的大置位3D对象跟踪
Large-displacement 3D Object Tracking with Hybrid Non-local Optimization
论文作者
论文摘要
已知基于优化的3D对象跟踪是精确且快速，但对大型框架间位移敏感。在本文中，我们提出了一种快速有效的非本地3D跟踪方法。基于观察到错误的局部最小值主要是由于平面外旋转，我们提出了一种混合方法，将非本地和局部优化的不同参数组合为不同参数，从而在6D姿势空间中有效地非本地搜索。此外，为姿势优化提出了一种预先计算的基于强大轮廓的跟踪方法。通过使用带有多个候选对应的长搜索线，它可以适应不同的帧位移而无需粗到精细的搜索。在预计算之前，可以非常快速地进行姿势更新，从而使非本地优化实时运行。我们的方法优于大小位移的所有先前方法。对于大型位移，精度得到了极大的提高（$ 81.7 \％\; \ text {v.s。} \; 19.4 \％$）。同时，只有CPU可以实现实时速度（$> $ 50fps）。源代码可在\ url {https://github.com/cvbubbles/nonlocal-3dtracking}中获得。
Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements. In this paper we propose a fast and effective non-local 3D tracking method. Based on the observation that erroneous local minimum are mostly due to the out-of-plane rotation, we propose a hybrid approach combining non-local and local optimizations for different parameters, resulting in efficient non-local search in the 6D pose space. In addition, a precomputed robust contour-based tracking method is proposed for the pose optimization. By using long search lines with multiple candidate correspondences, it can adapt to different frame displacements without the need of coarse-to-fine search. After the pre-computation, pose updates can be conducted very fast, enabling the non-local optimization to run in real time. Our method outperforms all previous methods for both small and large displacements. For large displacements, the accuracy is greatly improved ($81.7\% \;\text{v.s.}\; 19.4\%$). At the same time, real-time speed ($>$50fps) can be achieved with only CPU. The source code is available at \url{https://github.com/cvbubbles/nonlocal-3dtracking}.
