Global Parametrization of Range Image Sets

Nico Pietroni, Marco Tarini, Olga Sorkine-Hornung, Denis Zorin

Global Parametrization of Range Image Sets

Global parametrization of a point cloud. The input data is composed of 2.6M points.


We present a method to globally parameterize a surface represented by height maps over a set of planes (range images). In contrast to other parametrization techniques, we do not start with a manifold mesh. The parametrization we compute defines a manifold structure, it is seamless and globally smooth, can be aligned to geometric features and shows good quality in terms of angle and area preservation, comparable to current parametrization techniques for meshes. Computing such global seamless parametrization makes it possible to perform quad remeshing, texture mapping and texture synthesis and many other types of geometry processing operations. Our approach is based on a formulation of the Poisson equation on a manifold structure defined for the surface by the range images. Construction of such global parametrization requires only a way to project surface data onto a set of planes, and can be applied directly to implicit surfaces, nonmanifold surfaces, very large meshes, and collections of range scans. We demonstrate application of our technique to all these geometry types.



Global parameterization of 'bad' meshes

Meshes with topological problems can be handled with our method. Here, a triangle soup obtained by separating triangles and randomly scaling/rotating them is globally parameterized.

Global parameterization and quadriangulation results

Quadrangulation of a molecular model originally available as an implicit surface. The model was first rendered into rangemaps and then parameterized with our method.


The research leading to these results is partly funded by the EU Community’s FP7 ICT under the Project (Grant Agreement 270404), by NSF award IIS-0905502 and by a gift from Adobe Systems. We thank David Bommes, Marco Callieri, Matteo Dellepiane, AIM@SHAPE and Carlos Hernandez ( for providing several datasets.