Abstract
Augmented reality assembly guidance is essential for intelligent manufacturing and medical applications, requiring continuous measurement of the 6DoF poses of manipulated objects. Although current tracking methods have made significant advancements in accuracy and efficiency, they still face challenges in robustness when dealing with cluttered backgrounds, rotationally symmetric objects, and noisy sequences. In this paper, we first propose a robust contour-based pose tracking method that addresses error-prone contour correspondences and improves noise tolerance. It utilizes a fan-shaped search strategy to refine correspondences and models local contour shape and noise uncertainty as mixed probability distribution, resulting in a highly robust contour energy function. Secondly, we introduce a CPU-only strategy to better track rotationally symmetric objects and assist the contour-based method in overcoming local minima by exploring sparse interior correspondences. This is achieved by pre-sampling interior points from sparse viewpoint templates offline and using the DIS optical flow algorithm to compute their correspondences during tracking. Finally, we formulate a unified energy function to fuse contour and interior information, which is solvable using a re-weighted least squares algorithm. Experiments on public datasets and real scenarios demonstrate that our method significantly outperforms state-of-the-art monocular tracking methods and can achieve more than 100 FPS using only a CPU.
Abstract (translated)
增强现实装配指导在智能制造和医疗应用中至关重要，需要连续测量操作对象的6自由度（6DoF）姿态。尽管当前的跟踪方法已经在准确性和效率方面取得了显著进展，但在处理杂乱背景、旋转对称物体以及噪声序列时仍面临鲁棒性挑战。 本文首先提出了一种基于轮廓的稳健姿态跟踪方法，该方法解决了容易出错的轮廓对应关系，并提高了抗噪能力。它采用扇形搜索策略来细化对应关系，并将局部轮廓形状和噪声不确定性建模为混合概率分布，从而形成一个高度健壮的轮廓能量函数。 其次，我们引入了一种仅使用CPU的战略，以更好地跟踪旋转对称物体，并通过探索稀疏内部对应关系帮助基于轮廓的方法克服局部极小值问题。这通过在离线阶段从稀疏视点模板中预采样内部点并在追踪过程中利用DIS光流算法计算它们的对应关系来实现。 最后，我们提出了一个统一的能量函数来融合轮廓和内部信息，并使用重新加权最小二乘法求解该能量函数。 公开数据集和真实场景中的实验表明，与最先进的单目跟踪方法相比，我们的方法表现出显著的优势，并且仅使用CPU即可达到每秒100帧以上的性能。
URL
https://arxiv.org/abs/2502.11971
