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 shape coverage, uniformity, and centrality 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++. Our codes are available at https://github.com/Frank-ZY-Dou/Coverage_Axis.

翻译：我们提出覆盖轴++（Coverage Axis++），一种新颖且高效的3D形状骨架化方法。当前该领域的最先进方法往往依赖输入的封闭性，或面临显著的计算成本限制，从而制约了其实用性。为应对这一挑战，覆盖轴++提出了一种启发式算法来选取骨架点，在显著降低各类形状表示计算强度的同时，实现了对中轴变换（MAT）的高精度近似。我们引入了一种兼顾形状覆盖度、均匀性与中心性的简洁有效策略来推导骨架点。该选择过程在强化与形状结构一致性的同时，优先选取主导性中轴球，从而以中轴变换的形式生成紧凑的底层形状表示。因此，覆盖轴++支持多种形状表示（如封闭网格、三角形汤、点云）的骨架化，可指定骨架点数量，仅需少量超参数，并能在提升重建精度的同时实现高效计算。在广泛3D形状上的大量实验验证了覆盖轴++的效率与有效性。我们的代码开源在https://github.com/Frank-ZY-Dou/Coverage_Axis。