Curve Skeletonization in Continuous domain for Meshes and Point Clouds
2026-05-25 • Graphics
GraphicsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors present a new method called CSCD for making simpler line representations (skeletons) of 3D shapes that are more accurate than previous methods. They improved an existing approach by working directly with the continuous surfaces of 3D objects rather than discrete approximations. Their method has two versions: one for shapes made of connected triangles (meshes) and one for sets of points (point clouds), each designed to handle noise and preserve important shape details. The authors show their method works well on benchmark datasets and helps with tasks like recognizing object parts and shapes.
3D curve skeletonizationlocal separatorscontinuous domainmeshpoint cloudintrinsic triangulationtufted Laplaciansshape segmentationtopological preservation
Authors
Jai Bardhan, Ramya Hebbalaguppe, Aravind Udupa
Abstract
Advancements in 3D curve skeletonization are accelerating progress across a wide range of applications. However, developing robust skeletonization algorithms that capture intricate object details remains challenging. Skeletonization via Local Separators (LS) offers an efficient graph-based approach but suffers from representation inaccuracies due to its discrete nature. To address this, we introduce CSCD, a novel framework for Curve Skeletonization in the Continuous Domain, generalizing LS to manifolds. Specifically, we present two realizations: CSCD-M for meshes and CSCD-PC for point clouds. CSCD-M leverages the intrinsic triangulation of a mesh for resilience to noise and improved topological preservation, while CSCD-PC employs tufted Laplacians for enhanced robustness. To our knowledge, CSCD-M is the first intrinsic method for curve skeletonization. Our results show CSCD-M matches LS performance across diverse meshes and outperforms LS (TOG'21) on benchmarks like Thingi10k dataset. CSCD-PC qualitatively outperforms CoverageAxis++ (Eurographics'24) and EPCS (CAG'23). Finally, we demonstrate the efficacy of CSCD in a few downstream tasks: object classification, shape segmentation, identifying handles, tunnels, and constrictions in objects. Project Website: https://cscd-skel.pages.dev