如今在计算机视觉领域，基于结构光从2维图像重建物体的3维轮廓已成为研究热点。然而低分辨率、纹理受限、对光照敏感是此项技术实际应用的主要制约。由于结构光视觉的关键技术是编码解码，因此为了进行高分辨率可靠的3维形貌重建，提出了一种新的彩色编码结构光技术，用来对单幅图像进行3维重建。该技术首先设计一个基于De Bruijn序列的等距白色光条间隔的彩色光模板，同时采用K均值聚类的方法来匹配投影仪和摄像机平面之间对应的光条颜色。然后引入聚类的统计特征量，以自适应不同场景、纹理，使之能够在无控光照条件下实现光条边界局部的精确定位、模板正确匹配和解码。另外，针对静态场景，同一光模板相移方法还可以获取高分辨率的3维形貌，可适用于高密度重建任务。实验结果表明，该方法可在日常光照下重建高分辨率可靠3维模型，并可提高3维形貌获取的适用条件。

It is an increasingly important topic to reconstruct 3D shape from images by structured light vision. However, their practical applications are mainly limited to low resolution, texture and sensitivity of environment illumination. This paper proposes a new color coded structured light technique for robustly reconstructing shapes from a single image. This technique works by projecting a pattern based on De Bruijn stripes with white gaps. The correspondence problem is solved using a color classification algorithm. The statistical cluster parameters of the captured image are adopted so that it is adaptive to different scenes and contexts. In this paper, the stripe boundary is located accurately by local a searching method. Additionally, a scheme is presented to achieve dense shape reconstruction by shifting the same pattern. Practical experimental results are provided to demonstrate the performance of the proposed method.