基于二值几何编码图案的高精度结构光系统参数标定方法研究

该文提出了一种基于空间编码图案的结构光系统参数标定方法。与传统的基于棋盘格图案标 定策略不同的是，该文采用结构光编码图案实现了系统的高精度标定，具体实施步骤包括：(1)根据编码图案的几何分布特性提出了一种编码特征点检测算子，基于检测出的编码特征点构建拓扑结构，利 用仿射变换原理及双线性插值算法提取出编码几何元素图像；(2)将几何元素识别转化为监督分类问题，通过采集大量训练样本训练卷积神经网络，实现编码元素的准确识别和解码过程；(3)利用射影变 换原理建立相机像平面与投影机像平面之间的对应关系，利用此对应关系将标定板上棋盘格角点在相机像平面上的坐标转换至投影机像平面，最终实现了对相机和投影仪内外部参数的同时标定。标定结果显示，该方法对投影仪的标定重投影误差不超过 0.3 像素；三维重建实验结果显示，与传统标定方法相比，该文方法能够显著提升系统的标定和三维重建精度。

An Accurate Calibration Method for Structured Light System Using Binary Geometrical Pattern

In this paper, a binary geometrical pattern is used for the calibration of structured light system instead of conventional checkboard pattern. The pattern is designed based on pseudo-random coding theory, and the calibration procedure contains following steps. First, an accurate feature detector is proposed by utilizing the geometric property of the pattern elements. It is show that, with the proposed feature detector, feature points can be robustly localized with sub-pixel precision. Based on the extracted feature points, a topological structure is constructed to separate all the pattern elements. The pattern elements are extracted with affine transformation theory and bilinear interpolation. Secondly, to identify the pattern elements, the convolutional neural network technique is adopted, which is trained by collecting a large number of pattern element samples. After the decoding stage, code words of the feature points can be computed. According to the projective transformation principle, pattern feature points in the camera image plane can be transformed to the projector image plane with the corresponding code word. Finally, both intrinsic and extrinsic parameters of camera and projector can be calculated. Experimental results show that the re-projector error of projector calibration results can be controlled within 0.3 pixels. In comparison with conventional calibration methods, both calibration and 3D reconstruction precision can be improved by the proposed approach.