title: Partwise cross-parameterization via nonregular convex hull domains.
abstract: In this paper, we propose a novel partwise framework for cross-parameterization between 3D mesh models. Unlike most existing methods that use regular parameterization domains, our framework uses nonregular approximation domains to build the cross-parameterization. Once the nonregular approximation domains are constructed for 3D models, different (and complex) input shapes are transformed into similar (and simple) shapes, thus facilitating the cross-parameterization process. Specifically, a novel nonregular domain, the convex hull, is adopted to build shape correspondence. We first construct convex hulls for each part of the segmented model, and then adopt our convex-hull cross-parameterization method to generate compatible meshes. Our method exploits properties of the convex hull, e.g., good approximation ability and linear convex representation for interior vertices. After building an initial cross-parameterization via convex-hull domains, we use compatible remeshing algorithms to achieve an accurate approximation of the target geometry and to ensure a complete surface matching. Experimental results show that the compatible meshes constructed are well suited for shape blending and other geometric applications.
authors.affiliation: Peking University, Key Laboratory of Machine Perception (MOE), Room 2219, Science Building No. 2, Peking University, Beijing 100871, PR China. wuhy@cis.pku.edu.cn
authors.forename: Huai-Yu
authors.initials: HY
authors.lastname: Wu
authors.forename: Chunhong
authors.initials: C
authors.lastname: Pan
authors.forename: Hongbin
authors.initials: H
authors.lastname: Zha
authors.forename: Qing
authors.initials: Q
authors.lastname: Yang
authors.forename: Songde
authors.initials: S
authors.lastname: Ma