Date: June 2014.
Source: 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp 3794-3801, doi: 10.1109/CVPR.2014.491.
Abstract: New scanning technologies are increasing the importance of 3D mesh data and the need for algorithms that can reliably align it. Surface registration is important for building full 3D models from partial scans, creating statistical shape models, shape retrieval, and tracking. The problem is particularly challenging for non-rigid and articulated objects like human bodies. While the challenges of real-world data registration are not present in existing synthetic datasets, establishing ground-truth correspondences for real 3D scans is difficult. We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments. We define a new dataset called FAUST that contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology. To achieve accurate registration, we paint the subjects with high-frequency textures and use an extensive validation process to ensure accurate ground truth. We find that current shape registration methods have trouble with this real-world data. The dataset and evaluation website are available for research purposes at http://faust.is.tue.mpg.de.
3dMD’s Role: FAUST’s 300 human body scans of 10 subjects in 30 poses each, were captured using a static 3dMDbody22 active stereophotogrammetry system composed of 22 Modular Camera Units (MCUs), each with a stereo camera pair, one or two speckle projectors, and a 5MP color camera. This is the predecessor dataset to Dynamic FAUST, which used a dynamic 60fps 3dMDbody22.u system to extend this same body of work into dynamic-3D/4D dense-surface motion capture.
Article: FAUST: Dataset and Evaluation for 3D Mesh Registration.
Authors: Federica Bogo, Javier Romero, Matthew Loper, Michael J Black. Max Planck Institute for Intelligent Systems, Perceiving Systems, Tuebingen, Germany.