Feature-preserving simplification framework for 3D point cloud

To obtain a higher simplification rate while retaining geometric features, a simplification framework for the point cloud is proposed. Firstly, multi-angle images of the original point cloud are obtained with a virtual camera. Then, feature lines of each image are extracted by deep neural network. Furthermore, according to the proposed mapping relationship between the acquired 2D feature lines and original point cloud, feature points of the point cloud are extracted automatically. Finally, the simplified point cloud is obtained by fusing feature points and simplified non-feature points. The proposed simplification method is applied to four data sets and compared with the other six algorithms. The experimental results demonstrate that our proposed simplification method has the superiority in terms of both retaining geometric features and high simplification rate.

Automated summary

This study proposes a point cloud simplification framework that uses a virtual camera to obtain multi-angle images, extracts feature lines using deep neural networks, automatically extracts feature points of the point cloud based on a mapping relationship, and ultimately obtains a simplified point cloud. Experimental results demonstrate the superiority of this method in retaining geometric features and achieving a high simplification rate.