目前在零件模型上容易将低曲率圆柱面的局部区域识别为平面，并且只能做到一种图元的快速准确检测。基于此，提出一种能够同时对平面和圆柱面进行精确快速检测的面向点云数据的面图元快速检测方法。该方法分为粗识别和精化两阶段：首先，将点云划分为小粒度基片，计算基片特征，粗识别出平面基片或圆柱面基片；之后，根据过滤条件将圆柱面基片邻近的平面基片过滤，合并具有相同特征的基片得到完整平面和圆柱面。使用5个机械零件数据进行实验验证，并将其与目前流行的两种识别方法进行比较。结果表明，该方法不会出现其他两种方法存在的遗漏和错误识别现象，同时在多圆柱面相连时的准确分割以及曲面参数精度上，优于其他两种方法。

Current detection methods for three dimensional (3D) point cloud data easily identify the local area of low-curvature cylindrical surfaces as planes in a model, but these methods can achieve the fast and accurate identification of only a single element. We propose a fast primitive detection method for point cloud data that can quickly and accurately detect both planar and cylindrical surfaces simultaneously. The proposed method is divided into two stages: coarse recognition and refinement. First, the point cloud is divided into small-grained patches, the patch characteristics are calculated, and the planar and cylindrical patches are roughly identified. Next, according to the filter conditions, the planar patches adjacent to the cylindrical patches are filtered, and then the patches with identical characteristics are combined to obtain the complete planar and cylindrical surfaces. Our experiments show that the proposed method is superior to two popular recognition methods when used to analyze data concerning five mechanical components. Moreover, the proposed method does not exhibit the omission and misidentification errors demonstrated by the other two methods, and the proposed method is more accurate in terms of the surface parameter estimation and segmentation when multiple cylindrical surfaces are connected.