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Abstract |
We present a point cloud segmentation scheme based on estimated surface normals and local point connectivity, that operates on unstructured point cloud data. We can segment a point cloud into disconnected components as well as piecewise smooth components as needed. Given that the performance of the segmentation routine depends on the quality of the surface normal approximation, we also propose an improved surface normal approximation method based on recasting the popular principal component analysis formulation as a constrained least squares problem. The new approach is robust to singularities in the data, such as corners and edges, and also incorporates data denoising in a manner similar to planar moving least squares. |
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