Feature-Enhanced Surfaces from Incomplete Point Cloud with Segmentation and Curve Skeleton Information

Raw data of point cloud is often noisy and with topological defects (such as holes), which cause problems including faulty connection and inaccurate structure. As a result, the surface reconstruction of point cloud data is a highly challenging problem. This work proposes a novel method, which improves the surface quality compared with existing methods. Our method combines both the local detailed features and the global topological information during the reconstruction process. To facilitate the feature refinement, we first pre-process the point cloud data by relocating each point, upsampling the point data, and optimizing normals to enhance the features and geometric details. We then identify the topological information by segmenting the geometry and constructing curve skeletons for each part and guide the surface reconstruction with the skeletons by minimal user interaction. We demonstrate the effectiveness of our methods with various examples, where our reconstruction can fill out missing data and preserve sharp features.