3D Incomplete Point Cloud Surfaces Reconstruction With Segmentation and Feature-Enhancement.

Raw point cloud data are often noisy, superfluous, and with topological defects, such as holes. These issues cause inaccurate geometric representation in 3D reconstruction. As a result, surface reconstruction from point cloud data is a highly challenging problem. In this paper, we address the aforementioned issues by taking advantage of the embedded information of segmentation, skeletonization, and user guidance. First, we pre-process the point cloud data with three steps, relocating each point, upsampling the point data, and optimizing normals to enhance the features and geometric details; second, a segmentation method converts the input cloud into separate parts; finally, we construct curve skeletons for each part and guide the surface reconstruction with minimal user interaction, where the parts of refined smooth shapes are fused to generate the final results. The comparison studies confirmed that the proposed method is able to produce state-of-the-art results in terms of preserving sharp features, handling missing data, and requiring minimal user intervention.