Curve Networks for Surface Reconstruction

TL;DR

This paper introduces a framework for extracting and optimizing curve networks from unstructured point cloud data, aiding surface reconstruction especially in cases with missing data.

Contribution

The proposed method automatically extracts and refines curve networks from point clouds, improving surface reconstruction in incomplete or noisy data scenarios.

Findings

Effective extraction of curve networks from imperfect point clouds

Improved surface reconstruction quality using the extracted curves

User interface aids in completing and refining curve networks

Abstract

Man-made objects usually exhibit descriptive curved features (i.e., curve networks). The curve network of an object conveys its high-level geometric and topological structure. We present a framework for extracting feature curve networks from unstructured point cloud data. Our framework first generates a set of initial curved segments fitting highly curved regions. We then optimize these curved segments to respect both data fitting and structural regularities. Finally, the optimized curved segments are extended and connected into curve networks using a clustering method. To facilitate effectiveness in case of severe missing data and to resolve ambiguities, we develop a user interface for completing the curve networks. Experiments on various imperfect point cloud data validate the effectiveness of our curve network extraction framework. We demonstrate the usefulness of the extracted curve networks for surface reconstruction from incomplete point clouds.