散乱噪声点云的数据分割

DATA SEGMENTATION OF UNORGANIZED NOISE POINT-CLOUD

提出基于边界曲线微分几何特征的新方法分割散乱噪声点云。改进TAUBIN方法以精确恢复散乱噪声数据的主曲率和主方向。通过分析散乱点在主方向的曲率变化,达到识别G1、G2连续边界点的目的。获得的边界点形成边界带,将点云分割为多块子区域。最后采用区域增长的方法提取各子区域。试验结果表明所提出的方法能够克服噪声影响,有效提取散乱噪声点云的G1、G2边界。对复杂曲面模型,该方法也能够直接获得较好的G2连续边界。

Based on differential-geometry features of edge curves, a new data segmentation method is proposed to segment the unorganized noise point-cloud. An algorithm revised from the TAUBIN＇s paper is put forward first to estimate the principle curvatures and principle directions of the unorganized noise points. By analyze the variability of curvature in principle direction for each point, the G^1 or G^2 continuous edge-points can be detected. The acquired edge-points form into edge stripes, which segment the point-cloud into a few sub-regions. Finally a region-growing way is adopted to identify every, sub-area. Results indicate that the presented method can overcome noise influence and recognize the G^1 and G^2 edges of unorganized noise point-cloud effectively, and the method can directly acquire good G^2 edges of the complicated object.