Results and Rankings

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



Average error
SP_intra_challenge [1] 1.568 Visualize Results
Convex-Opt [2] 4.86 Visualize Results
CHARM [3] 15.157 Visualize Results
FMNet [4] 2.436 Visualize Results
FARM [5] 2.81 Visualize Results
3D-CODED : 3D Correspondences by Deep Deformation [6] 1.985 Visualize Results
LBS-AE (Unsupervised) [7] 2.161 Visualize Results
Unsupervised Learning of Dense Shape Correspondence [8] 2.51 Visualize Results
Unsupervised Learning of Dense Shape Correspondence [9] 2.818 Visualize Results
unsupervised 3D-CODED : 3D Correspondences by Deep Deformation [10] 2.789 Visualize Results
BPS [11] 2.327 Visualize Results
without learning 3D : Learning elementary structures for 3D shape generation and matching [12] 2.042 Visualize Results
Points Translation Learning 3D : Learning elementary structures for 3D shape generation and matching [13] 1.882 Visualize Results
Patch Deformation Learning 3D : Learning elementary structures for 3D shape generation and matching [14] 1.868 Visualize Results
Points Translation and Patch Deformation Learning 3D : Learning elementary structures for 3D shape generation and matching [15] 1.874 Visualize Results
References
[1]
SP_intra_challenge
S. Zuffi, M. J. Black, "The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose", CVPR, Boston, MA, June 2015.
[2]
Convex-Opt
Robust Nonrigid Registration by Convex Optimization. Qifeng Chen, Vladlen Koltun. International Conference on Computer Vision (ICCV), 2015
[3]
CHARM
[4]
FMNet
"Deep functional maps: Structured prediction for dense shape correspondence". Litany, Remez, Rodola, Bronstein, Bronstein. Proc. ICCV 2017
[5]
FARM
"FARM: Functional Automatic Registration Method for 3D Human Bodies". Marin, Melzi, Rodola, Castellani. arXiv:1807.10517, 2018.
[6]
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.
[7]
LBS-AE (Unsupervised)
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds, CVPR 2019
[8]
Unsupervised Learning of Dense Shape Correspondence
Unsupervised Learning of Dense Shape Correspondence, Oshri Halimi, Or Litany, Emanuele Rodola, Alex M. Bronstein, Ron Kimmel; (CVPR 2019)
[9]
Unsupervised Learning of Dense Shape Correspondence
Unsupervised Learning of Dense Shape Correspondence, Oshri Halimi, Or Litany, Emanuele Rodola, Alex M. Bronstein, Ron Kimmel; (CVPR 2019)
[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.
[11]
BPS
Anonymous.
[12]
without learning 3D : Learning elementary structures for 3D shape generation and matching
Learning elementary structures for 3D shape generation and matching, Deprelle, Theo, Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu, ECCV 2018
[13]
Points Translation Learning 3D : Learning elementary structures for 3D shape generation and matching
Learning elementary structures for 3D shape generation and matching, Deprelle, Theo, Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu, ECCV 2018
[14]
Patch Deformation Learning 3D : Learning elementary structures for 3D shape generation and matching
Learning elementary structures for 3D shape generation and matching, Deprelle, Theo, Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu, ECCV 2018
[15]
Points Translation and Patch Deformation Learning 3D : Learning elementary structures for 3D shape generation and matching
Learning elementary structures for 3D shape generation and matching, Deprelle, Theo, Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu, ECCV 2018