We propose a variational approach to computing an optimal segmentation of a 3D shape for computing a union of tight bounding volumes. Based on an affine invariant measure of e-ti...
TON: Curve Network Abstraction for 3D Shapes Fernando de Goesa,∗ , Siome Goldensteinb , Mathieu Desbruna , Luiz Velhoc aCalifornia Institute of Technology, Pasadena, CA 91125, US...
Fernando de Goes, Siome Goldenstein, Mathieu Desbr...
We present a novel approach to inferring 3D volumetric shape of both moving objects and static background from video sequences shot by a moving camera, with the assumption that th...
Most methods for the recognition of shape classes from 3D datasets focus on classifying clean, often manually generated models. However, 3D shapes obtained through acquisition tech...
Abstract. We present a novel method for the segmentation of volumetric images, which is especially suitable for highly variable soft tissue structures. Core of the algorithm is a s...