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++. 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3D形状骨架化方法。当前最先进的方法通常依赖于输入的封闭性，或面临显著的计算成本问题，从而限制了其实用性。为应对这一挑战，覆盖轴++提出一种启发式算法来选择骨架点，在显著降低计算强度的同时，为各种形状表示提供高精度的中轴变换（MAT）近似。我们引入一种简单而有效的策略，综合考虑形状覆盖率、均匀性和中心性来推导骨架点。该选择过程在约束与形状结构一致性的前提下，优先选取主导中轴球，从而以MAT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、三角网格集、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。通过在大范围3D形状上的广泛实验，验证了覆盖轴++的效率与有效性。论文发表后，相关代码将公开提供。