Results and Rankings

	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

^[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