A Geometric Algorithm for Tubular Shape Reconstruction from Skeletal Representation
This work provides a faster and more elegant solution for tubular shape reconstruction, which is useful for applications in computer graphics and medical imaging, though it appears incremental as it builds on existing skeletal representation methods.
The paper tackles the problem of reconstructing tubular shapes from skeletal representations by introducing a geometric algorithm that processes all skeletal points as a whole, eliminating the need for segmentation, and demonstrates efficiency and effectiveness in experiments.
We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations. Our method processes all skeletal points as a whole, eliminating the need for splitting input structure into multiple segments. We represent the tubular shape as a truncated signed distance function (TSDF) in a voxel hashing manner, in which the signed distance between a voxel center and the object is computed through a simple geometric algorithm. Our method does not involve any surface sampling scheme or solving large matrix equations, and therefore is a faster and more elegant solution for tubular shape reconstruction compared to other approaches. Experiments demonstrate the efficiency and effectiveness of the proposed method. Code is avaliable at https://github.com/wlsdzyzl/Dragon.