Visual Results by Method

Results for methods appear here after users upload them and approve them for public display.





Metrics


Average error Average error over all the ground-truth correspondences.
Error visualization: for each pair, the plot on the left reports the errors in cm for all the ground-truth correspondences (sorted by decreasing error).

For the intra-subject challenge, the figure on the right shows the error distribution over the vertices of the first scan in the pair (blue represents the minimum error, red represents the maximum error).

For the inter-subject challenge, the figure on the right shows the error distribution over different areas of the first scan in the pair (each area corresponds to a landmark location; black represents the minimum error, white represents the maximum error).


References
[1]
3D-CODED : 3D Correspondences by Deep Deformation
3D-CODED : 3D Correspondences by Deep Deformation, Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu, ECCV 2018.
[2]
Convex-Opt
Robust Nonrigid Registration by Convex Optimization. Qifeng Chen, Vladlen Koltun. International Conference on Computer Vision (ICCV), 2015
[3]
FARM
"FARM: Functional Automatic Registration Method for 3D Human Bodies". Marin, Melzi, Rodola, Castellani. arXiv:1807.10517, 2018.
[4]
FMNet
"Deep functional maps: Structured prediction for dense shape correspondence". Litany, Remez, Rodola, Bronstein, Bronstein. Proc. ICCV 2017
[5]
MGAN-Inter
Anonymous.
[6]
MGAN-Inter
Anonymous.
[7]
MGAN-intra
Anonymous.
[8]
MGAN-intra
Anonymous.
[9]
SP_inter_challenge
S. Zuffi, M. J. Black, "The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose", CVPR, Boston, MA, June 2015.
[10]
unsupervised 3D-CODED : 3D Correspondences by Deep Deformation
3D-CODED : 3D Correspondences by Deep Deformation, Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu, ECCV 2018.