Unorganized point classification for robust NURBS surface reconstruction using a point-based neural network

In this paper, a method for classifying 3D unorganized points into interior and boundary points using a deep neural network is proposed. The classification of 3D unorganized points into boundary and interior points is an important problem in the nonuniform rational B-spline (NURBS) surface reconstruction process. A part of an existing neural network PointNet, which processes 3D point segmentation, is used as the base network model. An index value corresponding to each point is proposed for use as an additional property to improve the classification performance of the network. The classified points are then provided as inputs to the NURBS surface reconstruction process, and it has been demonstrated that the reconstruction is performed efficiently. Experiments using diverse examples indicate that the proposed method achieves better performance than other existing methods.

Automated summary

This paper proposes a method for classifying 3D unorganized points into interior and boundary points using a deep neural network, which is applied to the NURBS surface reconstruction process. Experimental results demonstrate that the proposed method achieves better performance than other existing methods.