Ji Xiu, Yang Huamin, Han Cheng, Xu Jiayu, Wang Yan
Appl Opt. 2021 Dec 10;60(35):10901-10913. doi: 10.1364/AO.440738.
Three-dimensional (3D) registration plays a pivotal step in augmented reality (AR) systems. Traditional 3D registration methods have the disadvantages of poor accuracy and robustness. This paper proposes a novel registration method, we believe, for AR systems based on the AKAZE and Tanimoto similarity measurement method. In this paper, the image feature points are extracted and matched by combining the AKAZE algorithm and the Tanimoto similarity measurement method. Then, the camera pose is estimated by calculating the constraint relationship of the feature points. Finally, the 3D registration and real-time tracking of the virtual objects are realized by the Lucas-Kanade (LK) optical flow tracking algorithm. We use Tanimoto to determine the similarity of feature points to improve the matching accuracy of the AKAZE algorithm, and this method not only retains the advantages of strong scale adaptation but also has the advantage of high-precision matching. Experiments show that the method proposed in this paper has the benefits of high registration accuracy, low time consumption, and strong robustness. Under the premise of ensuring accuracy, when the marker is rotated or blocked, it can be accurately registered. In addition, when the external environment changes, for example, the light intensity or the size of the parallax, the registration can still be stable.
三维（3D）配准在增强现实（AR）系统中起着关键作用。传统的3D配准方法存在精度差和鲁棒性不足的缺点。本文提出了一种新颖的、基于AKAZE和谷本相似度测量方法的AR系统配准方法。在本文中，通过结合AKAZE算法和谷本相似度测量方法来提取和匹配图像特征点。然后，通过计算特征点的约束关系来估计相机姿态。最后，通过Lucas-Kanade（LK）光流跟踪算法实现虚拟物体的3D配准和实时跟踪。我们使用谷本系数来确定特征点的相似度，以提高AKAZE算法的匹配精度，该方法不仅保留了尺度适应性强的优点，还具有高精度匹配的优势。实验表明，本文提出的方法具有配准精度高、时间消耗低和鲁棒性强的优点。在确保精度的前提下，当标记旋转或被遮挡时，仍能准确配准。此外，当外部环境变化时，例如光强或视差大小变化时，配准仍能保持稳定。
