GLSkeleton: A Geometric Laplacian-Based Skeletonisation Framework for Object Point Clouds

摘要

The curve skeleton is known to geometric modelling and computer graphics communities as one of the shape descriptors which intuitively indicates the topological properties of the objects. In recent years, studies have also suggested the potential of applying curve skeletons to assist robotic reasoning and planning. However, the raw scanned point cloud model is typically incomplete and noisy. Besides, dealing with a large point cloud is also computationally inefficient. Focusing on the curve skeletonisation of incomplete and poorly distributed point clouds of objects, an efficient geometric Laplacian-based skeletonisation framework (GLSkeleton) is proposed in this work. We also present the computational efficiency of the introduced local reduction strategy (LPR) approach without sacrificing the main topological structure. Comprehensive experiments have been conducted to benchmark performance using an open-source dataset, and they have demonstrated a significant improvement in both contraction and overall skeletonisation computational speed.