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++，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输入的水密性或者承受显著的计算成本，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，能够以高精度逼近中轴变换，同时显著降低多种形状表示的计算强度。我们引入一种简单而有效的策略，同时考虑形状覆盖度和均匀性来推导骨架点。选择过程强制与形状结构保持一致，同时优先考虑主导中轴球，从而在MAT框架下生成紧凑的底层形状表示。因此，覆盖轴++支持多种形状表示（如水密网格、三角网格Soup、点云）的骨架化，可指定骨架点数量、使用极少超参数，并以更高的重建精度实现高效计算。在广泛3D形状上的大量实验验证了覆盖轴++的效率和有效性。代码将在论文发表后公开。