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FAUST: Dataset and evaluation for 3D mesh registration


Conference Paper


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.

Award: (Dataset Award, Eurographics Symposium on Geometry Processing (SGP), 2016)
Author(s): Federica Bogo and Javier Romero and Matthew Loper and Michael J. Black
Book Title: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Pages: 3794 --3801
Year: 2014
Month: June

Department(s): Perzeptive Systeme
Research Project(s): 3D Mesh Registration
FAUST dataset
Human Pose, Shape and Action
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: http://dx.doi.org/10.1109/CVPR.2014.491
Event Name: IEEE International Conference on Computer Vision and Pattern Recognition
Event Place: Columbus, Ohio, USA

Address: Columbus, Ohio, USA
Award Paper: Dataset Award, Eurographics Symposium on Geometry Processing (SGP), 2016

Links: pdf


  title = {{FAUST}: Dataset and evaluation for {3D} mesh registration},
  author = {Bogo, Federica and Romero, Javier and Loper, Matthew and Black, Michael J.},
  booktitle = { Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  pages = {3794  --3801},
  address = {Columbus, Ohio, USA},
  month = jun,
  year = {2014},
  month_numeric = {6}