Region Based User-Generated Human Body Scan Registration

We present a region-based registration method to robustly register low-quality human body scans that are acquired with cost-effective devices accessible to general users. When traditional closest point based registration approaches are performed on these noisy scan data, it is easy to fall into local minimum. To address this problem, we learn prior knowledge of body shape from publicly available dataset and combine it with the Iterative Closest Point (ICP) algorithm. Firstly, sparse markers are used to change pose of template, making it perform in the same way as target scans do. In the registration stage, the holistic shape model for the basic figure of human and a set of local shape models for describing the details of each human body part are trained. We fit the holistic model roughly to the target mesh. To capture more body details, we combine local shape models with the non-rigid ICP method to deform the template part-by-part. Extensive experiments over data scanned using devices from professional to low-cost types verify that our approach is both accurate and robust to incomplete and noisy data.