Multilevel active registration for kinect human body scans: from low quality to high quality
This addresses the challenge of automating 3D human body registration for low-cost scans, making it more accessible, though it is incremental as it builds on existing registration techniques.
The paper tackles the problem of registering 3D human body scans from low-quality devices like Kinect, which have holes and noise, by proposing a fully automatic active registration method that deforms a high-resolution template mesh using a two-level statistical shape model, achieving results comparable to state-of-the-art methods.
Registration of 3D human body has been a challenging research topic for over decades. Most of the traditional human body registration methods require manual assistance, or other auxiliary information such as texture and markers. The majority of these methods are tailored for high-quality scans from expensive scanners. Following the introduction of the low-quality scans from cost-effective devices such as Kinect, the 3D data capturing of human body becomes more convenient and easier. However, due to the inevitable holes, noises and outliers in the low-quality scan, the registration of human body becomes even more challenging. To address this problem, we propose a fully automatic active registration method which deforms a high-resolution template mesh to match the low-quality human body scans. Our registration method operates on two levels of statistical shape models: (1) the first level is a holistic body shape model that defines the basic figure of human; (2) the second level includes a set of shape models for every body part, aiming at capturing more body details. Our fitting procedure follows a coarse-to-fine approach that is robust and efficient. Experiments show that our method is comparable with the state-of-the-art methods.