Medial Axis

The medial axis, a skeleton-like representation of a shape's interior, is a key concept in shape analysis with applications ranging from medical imaging to porous media modeling. Current research focuses on developing efficient and accurate algorithms for medial axis approximation, employing techniques like neural networks (e.g., convolutional networks and ray fields) and heuristic approaches to improve computational speed and handle complex shapes, including those with varying densities and topological complexities. These advancements enable improved shape reconstruction, visualization, and simulation in various fields, offering more robust and efficient tools for analyzing complex three-dimensional structures.