Region-of-interest volumetric visual hull refinement

This paper introduces a region-of-interest visual hull refinement technique, based on flexible voxel grids for volumetric visual hull reconstructions. Region-of-interest refinement is based on a multipass process, beginning with a focussed visual hull reconstruction, resulting in a first 3D approximation of the target, followed by a region-of-interest estimation, tasked with identifying features of interest, which in turn are used to locally refine the voxel grid and extract a higher-resolution surface representation for those regions. This approach is illustrated for the reconstruction of avatars for use in tele-immersion environments, where head and hand regions are of higher interest. To allow reproducability and direct comparison a publicly available data set for human visual hull reconstruction is used. This paper shows that region-of-interest reconstruction of the target is faster and visually comparable to higher resolution focused visual hull reconstructions. This approach reduces the amount of data generated through the reconstruction, allowing faster post processing, as rendering or networking of the surface voxels. Reconstruction speeds support smooth interactions between the avatar and the virtual environment, while the improved resolution of its facial region and hands creates a higher-degree of immersion and potentially impacts the perception of body language, facial expressions and eye-to-eye contact. Copyright © 2010 by the Association for Computing Machinery, Inc.