Ha Minhtuan, Pham Dieuthuy, Xiao Changyan
Appl Opt. 2021 Apr 10;60(11):2926-2937. doi: 10.1364/AO.414952.
The 3D imaging methods using a grid pattern can satisfy real-time applications since they are fast and accurate in decoding and capable of producing a dense 3D map. However, like the other spatial coding methods, it is difficult to achieve high accuracy as is the case for time multiplexing due to the effects of the inhomogeneity of the scene. To overcome those challenges, this paper proposes a convolutional-neural-network-based method of feature point detection by exploiting the line structure of the grid pattern projected. First, two specific data sets are designed to train the model to individually extract the vertical and horizontal stripes in the image of a deformed pattern. Then the predicted results of trained models with images from the test set are fused in a unique skeleton image for the purpose of detecting feature points. Our experimental results show that the proposed method can achieve higher location accuracy in feature point detection compared with previous ones.
使用网格图案的3D成像方法能够满足实时应用，因为它们在解码方面快速且准确，并且能够生成密集的3D地图。然而，与其他空间编码方法一样，由于场景不均匀性的影响，很难像时分复用那样实现高精度。为了克服这些挑战，本文提出了一种基于卷积神经网络的特征点检测方法，该方法利用投影网格图案的线条结构。首先，设计两个特定的数据集来训练模型，以分别提取变形图案图像中的垂直条纹和水平条纹。然后，将训练模型对测试集图像的预测结果融合到一个独特的骨架图像中，以检测特征点。我们的实验结果表明，与以前的方法相比，该方法在特征点检测中能够实现更高的定位精度。
