DOE-based structured-light method for accurate 3D sensing
DOE-based structured-light method for accurate 3D sensing
复制标题
基于 DOE 的结构光方法，实现精确 3D 传感
DOI：
10.1016/j.optlaseng.2019.02.009
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发表时间：
2019-09-01
影响因子：
4.6
通讯作者：
Feng, Jianyang
中科院分区：
文献类型：
--
作者：
Song, Zhan;Tang, Suming;Feng, Jianyang
本文介绍了一种采用衍射光学元件作为投影装置的紧凑、精确的三维传感系统。与传统的基于激光散斑的三维传感方法相比，该方法采用网格线模式代替点模式。所提出的模式是根据伪随机编码方案设计的，并将八个几何元素嵌入到网格单元中，为每个定义的网格点形成唯一的码字。利用所提出的特征检测器提取网格点，建立拓扑图来分离每个模式元素。训练卷积神经网络对投影模式元素进行鲁棒识别。最后，采用码字校正程序对解码结果进行优化。利用所提出的系统标定方法，可以对解码后的网格点进行精确的三维重建。测量平面度和步距的绝对平均误差仅为0.2 ~ 0.3 mm，远高于传统激光散斑传感器的测量精度。为了证明所提出的解码算法的鲁棒性，使用了具有丰富颜色和纹理的目标。结果表明，该方法能够对大部分网格点进行鲁棒性检测，并能对人脸、人体等复杂表面进行精确重构。
This paper presents a compact and accurate three-dimensional (3D) sensing system that employs a diffraction optical element as a projection device. Compared with the conventional laser speckle-based 3D sensing methods, a gridline pattern is utilized instead of a dot pattern. The proposed pattern is designed according to a pseudorandom coding scheme, and eight geometrical elements are embedded into the grid cells to form a unique codeword for each defined grid-point. By extracting the grid-points with the proposed feature detector, a topological graph is established to separate each pattern element. A convolutional neural network is trained for robust identification of the projected pattern elements. Finally, a codeword-correction procedure is applied to refine the decoding results. Using the proposed system-calibration method, accurate 3D reconstruction can be realized for the decoded grid-points. The measurement of the planarity and step distance has an absolute mean error of only 0.2-0.3 mm, indicating that it is far more accurate than the measurement using classical laser speckle-based 3D sensors. To demonstrate robustness of the proposed decoding algorithms, targets with plentiful color and texture are used. The results show that most of the grid-points can be robustly detected and that complex surfaces such as human faces and bodies can be precisely reconstructed.