Cavity Three-Dimensional Laser Scanning Point Cloud Data Processing Technology

doi:10.12068/j.issn.1005-3026.2016.11.024

Abstract

Abstract: Aiming at noise points in point cloud data detected by three-dimensional laser in cavity， curvature-chord ratio composite criterion was put forward for filtering and simplifying significant noise points. Besides， random filter algorithm was applied to reduce low frequency random noise points similar to the change of object. The piecewise low-order interpolation method was applied to fit cavity point cloud based on reduction and simplifying， thus the curve became smoother than before and after treatment. The application results show that the noise points in point cloud data detected by three-dimensional laser in cavity are effectively removed by filtering and smoothing treatment. Furthermore， the self-intersection in model is avoided. Therefore， cavity models generated by processed point cloud data are extremely identical to engineering practice.

Key words: cavity, three-dimensional laser scanning, point cloud data, noise filtering, smoothing treatment

CLC Number:

TD76

QIN Ya-guang， LUO Zhou-quan， WANG Wei， ZHENG Kai-huan. Cavity Three-Dimensional Laser Scanning Point Cloud Data Processing Technology[J]. Journal of Northeastern University Natural Science, 2016, 37(11): 1635-1639.

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