We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state-of-the-art approaches for this task often rely on the watertightness of the input or suffer from substantial computational costs, thereby limiting their practicality. To address this challenge, Coverage Axis++ proposes a heuristic algorithm to select skeletal points, offering a high-accuracy approximation of the Medial Axis Transform (MAT) while significantly mitigating computational intensity for various shape representations. We introduce a simple yet effective strategy that considers both shape coverage and uniformity to derive skeletal points. The selection procedure enforces consistency with the shape structure while favoring the dominant medial balls, which thus introduces a compact underlying shape representation in terms of MAT. As a result, Coverage Axis++ allows for skeletonization for various shape representations (e.g., water-tight meshes, triangle soups, point clouds), specification of the number of skeletal points, few hyperparameters, and highly efficient computation with improved reconstruction accuracy. Extensive experiments across a wide range of 3D shapes validate the efficiency and effectiveness of Coverage Axis++. The code will be publicly available once the paper is published.

翻译：我们提出覆盖轴++（Coverage Axis++），一种新颖且高效的三维形状骨架化方法。当前该任务的主流方法往往依赖于输入的闭合性，或承受显著的计算开销，从而限制了其实用性。为解决这一挑战，覆盖轴++提出了一种启发式算法来选择骨架点，能够在显著降低各类形状表示计算强度的同时，实现对中轴变换（MAT）的高精度逼近。我们引入了一种兼顾形状覆盖度与均匀性的简单而有效的策略来获取骨架点。该选择过程强制执行与形状结构的一致性，同时优先选取主导中轴球，从而以中轴变换的形式导出紧凑的底层形状表示。因此，覆盖轴++支持多种形状表示的骨架化（如闭合网格、三角形网格簇、点云），可指定骨架点数量、参数极少，且能以更高重建精度实现高效计算。在多种三维形状上的大量实验验证了覆盖轴++的效能与效率。论文发表后，相关代码将公开提供。