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

- All pairs, Average error = 2.120
- Pair 00, Average error = 0.993
- Pair 01, Average error = 3.450
- Pair 02, Average error = 1.396
- Pair 03, Average error = 1.530
- Pair 04, Average error = 1.786
- Pair 05, Average error = 3.298
- Pair 06, Average error = 1.534
- Pair 07, Average error = 1.997
- Pair 08, Average error = 1.686
- Pair 09, Average error = 3.410
- Pair 10, Average error = 1.942
- Pair 11, Average error = 0.792
- Pair 12, Average error = 1.254
- Pair 13, Average error = 1.003
- Pair 14, Average error = 2.143
- Pair 15, Average error = 1.483
- Pair 16, Average error = 1.594
- Pair 17, Average error = 11.222
- Pair 18, Average error = 1.589
- Pair 19, Average error = 1.114
- Pair 20, Average error = 1.952
- Pair 21, Average error = 2.884
- Pair 22, Average error = 2.735
- Pair 23, Average error = 1.673
- Pair 24, Average error = 1.100
- Pair 25, Average error = 1.177
- Pair 26, Average error = 2.716
- Pair 27, Average error = 1.535
- Pair 28, Average error = 1.541
- Pair 29, Average error = 6.476
- Pair 30, Average error = 5.702
- Pair 31, Average error = 1.043
- Pair 32, Average error = 7.670
- Pair 33, Average error = 1.724
- Pair 34, Average error = 1.457
- Pair 35, Average error = 1.676
- Pair 36, Average error = 1.105
- Pair 37, Average error = 1.308
- Pair 38, Average error = 1.154
- Pair 39, Average error = 1.084
- Pair 40, Average error = 3.398
- Pair 41, Average error = 5.593
- Pair 42, Average error = 2.865
- Pair 43, Average error = 1.920
- Pair 44, Average error = 4.127
- Pair 45, Average error = 2.188
- Pair 46, Average error = 2.019
- Pair 47, Average error = 1.753
- Pair 48, Average error = 2.885
- Pair 49, Average error = 1.136
- Pair 50, Average error = 1.336
- Pair 51, Average error = 6.831
- Pair 52, Average error = 2.772
- Pair 53, Average error = 1.315
- Pair 54, Average error = 3.705
- Pair 55, Average error = 1.407
- Pair 56, Average error = 1.782
- Pair 57, Average error = 2.109
- Pair 58, Average error = 3.870
- Pair 59, Average error = 3.591

Average 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).

- JOMS-inter-combined Anonymous.

- JOMS-inter-unisex Anonymous.

- 2icp

- 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.

- Boot20Gen Anonymous.

- Boot20Gen Anonymous.

- Boot20Gen Anonymous.

- Boot20Interp Anonymous.

- Boot20Interp Anonymous.

- Boot20Interp Anonymous.

- Boot80Gen Anonymous.

- Boot80Interp Anonymous.

- Boot80Interp Anonymous.

- BPS Efficient Learning on Point Clouds with Basis Point Sets

- CHARM

- combine-test

- Convex-Opt Robust Nonrigid Registration by Convex Optimization. Qifeng Chen, Vladlen Koltun. International Conference on Computer Vision (ICCV), 2015

- DHNN_ours_e1 Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network. IEEE TVCG 2020

- DHNN_ours_e2 Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network. IEEE TVCG 2020

- DHNN_ours1 Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network. IEEE TVCG 2020

- DHNN_ours2 Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network. IEEE TVCG 2020

- FARM "FARM: Functional Automatic Registration Method for 3D Human Bodies". Marin, Melzi, Rodola, Castellani. arXiv:1807.10517, 2018.

- fixed mesh Use unsurpervised loss. 0, true, 0 fixed meshes

- FMNet "Deep functional maps: Structured prediction for dense shape correspondence". Litany, Remez, Rodola, Bronstein, Bronstein. Proc. ICCV 2017

- george intra

- george_test_folder Anonymous.

- george_test_folder Anonymous.

- george_test_folder Anonymous.

- george_test_folder Anonymous.

- george_test_folder Anonymous.

- george_test_folder Anonymous.

- Inter Test FT2 LR Fr Anonymous.

- Inter Test FT2 LR Fr Anonymous.

- Intra Test Fr Anonymous.

- Intra Test Fr Anonymous.

- Intra Test Fr Anonymous.

- Intra Test FT2 LR Fr Anonymous.

- JOMS-inter-female Anonymous.

- JOMS-inter-male Anonymous.

- JOMS-intra-combined Anonymous.

- JOMS-intra-female Anonymous.

- JOMS-intra-male Anonymous.

- JOMS-intra-unisex Anonymous.

- LBS-AE (Unsupervised) LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds, CVPR 2019

- 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

- Reproduce FMNet

- reproduce UFMNet

- reproduce UFMNet 1 with 10, true, 10 Oshri data

- reproduce UFMNet 2 20, true, 20 Oshri data

- 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.

- test-2icp-2refine

- try_inter Anonymous.

- try_inter Anonymous.

- 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.

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

- 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)

- 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)