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：本文介绍覆盖轴++（Coverage Axis++），一种新颖且高效的3D形状骨架化方法。当前该领域最先进的算法往往依赖于输入模型的封闭性（watertightness），或面临巨大的计算代价，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用于选取骨架点，能在显著降低不同形状表示方法计算复杂度的同时，高质量逼近中轴变换（MAT）。我们引入一种兼顾形状覆盖度与均匀性的简单有效策略来提取骨架点。该选择过程在保留与形状结构一致性的前提下，优先选择主导性中轴球，从而以中轴变换形式构建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、三角形面片集、点云）的骨架化，可指定骨架点数量，仅需少量超参数，并在提升重建精度的同时实现高效计算。在多种3D形状上的大量实验验证了覆盖轴++的效率和有效性。论文发表后相关代码将公开提供。