Accurate and Fast Primitive Detection Method for 3D Point Cloud Data
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.
3D point cloudprimitive detectionregional growthmechanical partprimitive characteristics
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