Zhang Jie, Sun Junhua, Zhang Zhou
Opt Express. 2020 May 11;28(10):15611-15624. doi: 10.1364/OE.394766.
Identifying multiple line-structured lights from an image is a fundamental yet challenging issue in the active 3D visual reconstruction. The existing approaches using complex coding schemes are typically time-consuming and inapplicable to real-time sparse 3D reconstruction. In this paper, we solve the multi-line ambiguity from a new viewpoint-distribution pattern of the light segments in the image. We construct a local-to-global graph framework to fully describe the hierarchical distribution of multiple line-structured lights in a 2D image. The lights are firstly grouped as several local graphs according to a light overlapping metric. Then, the hierarchies of the local graphs are unified via the depth of the node, leading to a global graph. The lights in the same level of the global graph come from the same laser plane. The experimental results show the applicability of the proposed algorithm to identify scattered light segments and the robustness to varying sensor poses. We further apply the proposed algorithm to a 3D reconstruction case, achieving a reconstruction precision of 0.025mm. The proposed approach avoids complex auxiliary laser coding and thus is more convenient to conduct.
从图像中识别多条线结构光在主动式3D视觉重建中是一个基本但具有挑战性的问题。现有的使用复杂编码方案的方法通常耗时且不适用于实时稀疏3D重建。在本文中，我们从图像中光段的新视角分布模式解决多线模糊性问题。我们构建了一个从局部到全局的图框架来充分描述二维图像中多条线结构光的层次分布。首先根据光重叠度量将光分组为几个局部图。然后，通过节点深度统一局部图的层次结构，得到一个全局图。全局图同一级别的光来自同一激光平面。实验结果表明了所提算法识别散射光段的适用性以及对不同传感器姿态的鲁棒性。我们进一步将所提算法应用于3D重建案例，实现了0.025mm的重建精度。所提方法避免了复杂的辅助激光编码，因此更便于实施。
