Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization
arxiv(2024)
摘要
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.
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