The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
FAUST: Dataset and Evaluation for 3D Mesh Registration
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.
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Cites
SCAPE: shape completion and animation of people
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073207 · Cited by 1,489
The space of human body shapes: reconstruction and parameterization from range scans
Allen, B., Curless, B., & Popović, Z. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882311 · Cited by 913
Robust single-view geometry and motion reconstruction
Li, H., Adams, B., Guibas, L. J., & Pauly, M. (2009) · ACM Transactions on Graphics · doi:10.1145/1618452.1618521 · Cited by 334
Möbius voting for surface correspondence
Lipman, Y., & Funkhouser, T. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531378 · Cited by 332
One Point Isometric Matching with the Heat Kernel
Ovsjanikov, M., Mérigot, Q., Mémoli, F., & Guibas, L. (2010) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2010.01764.x · Cited by 301
Kim, V. G., Lipman, Y., & Funkhouser, T. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964974 · Cited by 298
Cited by
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734
Self-citationGeometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., & Bronstein, M. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.576 · Cited by 1,820
MeshCNN: a network with an edge
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., & Cohen-Or, D. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322959 · Cited by 742
Geodesic Convolutional Neural Networks on Riemannian Manifolds
Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.112 · Cited by 721
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Fey, M., Lenssen, J. E., Weichert, F., & Muller, H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00097 · Cited by 459
Metadata
- Authors
- Bogo, F., Romero, J., Loper, M., & Black, M. J.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 3794-3801
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1985907520
- Cited by
- 689
- References
- 6
- FWCI
- 63.31
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bogo, F., Romero, J., Loper, M., & Black, M. J. (2014). FAUST: Dataset and Evaluation for 3D Mesh Registration. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.491
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