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
Learning elementary structures for 3D shape generation and matching [12]	1.626	Visualize Results
Unsupervised Cyclic mapper intra [13]	2.12	Visualize Results
JOMS-intra-female [14]	2.353	Visualize Results
george intra [15]	6.737	Visualize Results
2icp [16]	6.724	Visualize Results
test-2icp-2refine [17]	12.746	Visualize Results
combine-test [18]	10.158	Visualize Results
Reproduce FMNet [19]	10.885	Visualize Results
reproduce UFMNet [20]	5.153	Visualize Results
fixed mesh [21]	4.531	Visualize Results
reproduce UFMNet 1 [22]	12.079	Visualize Results
reproduce UFMNet 2 [23]	12.053	Visualize Results
DHNN_ours2 [24]	1.369	Visualize Results
DHNN_ours1 [25]	1.395	Visualize Results
DHNN_ours_e1 [26]	1.17	Visualize Results
DHNN_ours_e2 [27]	1.011	Visualize Results
PAI_GCN [28]	2.037	Visualize Results
Intra Test Fr [29]	2.256	Visualize Results
Intra Test FT2 LR Fr [30]	2.251	Visualize Results
JOMS-intra-unisex [31]	2.304	Visualize Results
JOMS-intra-male [32]	2.387	Visualize Results
JOMS-intra-combined [33]	2.301	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

Efficient Learning on Point Clouds with Basis Point Sets

^[12]

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]

Unsupervised Cyclic mapper intra

Cyclic Functional Mapping: Self-supervised correspondence between non-isometric deformable shapes

^[14]

JOMS-intra-female

Anonymous.

^[15]

george intra

^[16]

2icp

^[17]

test-2icp-2refine

^[18]

combine-test

^[19]

Reproduce FMNet

^[20]

reproduce UFMNet

^[21]

fixed mesh

Use unsurpervised loss. 0, true, 0 fixed meshes

^[22]

reproduce UFMNet 1

with 10, true, 10 Oshri data

^[23]

reproduce UFMNet 2

20, true, 20 Oshri data

^[24]

DHNN_ours2

Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network. IEEE TVCG 2020

^[25]

DHNN_ours1

Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network. IEEE TVCG 2020

^[26]

DHNN_ours_e1

Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network. IEEE TVCG 2020

^[27]

DHNN_ours_e2

Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network. IEEE TVCG 2020

^[28]

PAI_GCN

TBD TBD

^[29]

Intra Test Fr

Anonymous.

^[30]

Intra Test FT2 LR Fr

Anonymous.

^[31]

JOMS-intra-unisex

Anonymous.

^[32]

JOMS-intra-male

Anonymous.

^[33]

JOMS-intra-combined

Anonymous.