Abstract
While the ready availability of 3D scan data has influenced research throughout computer vision, less attention
has focused on 4D data; that is 3D scans of moving nonrigid objects, captured over time. To be useful for vision
research, such 4D scans need to be registered, or aligned,
to a common topology. Consequently, extending mesh registration methods to 4D is important. Unfortunately, no
ground-truth datasets are available for quantitative evaluation and comparison of 4D registration methods. To address this we create a novel dataset of high-resolution 4D
scans of human subjects in motion, captured at 60 fps. We
propose a new mesh registration method that uses both 3D
geometry and texture information to register all scans in
a sequence to a common reference topology. The approach
exploits consistency in texture over both short and long time
intervals and deals with temporal offsets between shape and
?The work was performed at the MPI for Intelligent Systems.