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

- All pairs, Average error = 3.422
- Pair 00, Average error = 3.028
- Pair 01, Average error = 2.555
- Pair 02, Average error = 3.721
- Pair 03, Average error = 6.498
- Pair 04, Average error = 5.115
- Pair 05, Average error = 3.675
- Pair 06, Average error = 3.179
- Pair 07, Average error = 4.280
- Pair 08, Average error = 2.798
- Pair 09, Average error = 2.825
- Pair 10, Average error = 5.371
- Pair 11, Average error = 8.450
- Pair 12, Average error = 2.569
- Pair 13, Average error = 2.829
- Pair 14, Average error = 4.405
- Pair 15, Average error = 3.193
- Pair 16, Average error = 2.829
- Pair 17, Average error = 1.700
- Pair 18, Average error = 2.780
- Pair 19, Average error = 4.974
- Pair 20, Average error = 3.846
- Pair 21, Average error = 2.653
- Pair 22, Average error = 3.053
- Pair 23, Average error = 2.507
- Pair 24, Average error = 3.531
- Pair 25, Average error = 3.896
- Pair 26, Average error = 1.898
- Pair 27, Average error = 1.963
- Pair 28, Average error = 2.539
- Pair 29, Average error = 2.942
- Pair 30, Average error = 2.613
- Pair 31, Average error = 2.633
- Pair 32, Average error = 6.740
- Pair 33, Average error = 2.636
- Pair 34, Average error = 1.772
- Pair 35, Average error = 1.946
- Pair 36, Average error = 4.642
- Pair 37, Average error = 3.837
- Pair 38, Average error = 3.186
- Pair 39, Average error = 2.918

Average error | Average error over all the ground-truth correspondences. |

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.

- 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.

- BPS Efficient Learning on Point Clouds with Basis Point Sets

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

- Deep Virtual Marker (multi-view) Anonymous.

- Deep Virtual Marker (multi-view) Anonymous.

- Deep Virtual Marker (one-shot) Anonymous.

- Deep Virtual Marker (one-shot) Anonymous.

- 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.

- Faust-3 Anonymous.

- Faust-7 Anonymous.

- Faust3+Bootstrapped(3065) Anonymous.

- Faust3+Bootstrapped(3065) Anonymous.

- Faust3+Bootstrapped(3065) Anonymous.

- Faust3+Bootstrapped(3065) Anonymous.

- Faust7+Bootstrapped(3573) Anonymous.

- Faust7+Bootstrapped(3573) Anonymous.

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

- 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 Fr

- 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

- LSA-Conv: Learning localneighboring structure for robust 3d shape representation Z. Gao, J. Yan, G. Zhai, J. Zhang, Y. Yang, and X. Yang, “Learning localneighboring structure for robust 3d shape representation,” inThirty-FifthAAAI Conference on Artificial Intelligence, 2021. TBD

- PAI_GCN TBD TBD

- PGMNet Anonymous.

- PGMNet_Inter Anonymous.

- Smooth Shells Smooth Shells: Multi-Scale Shape Registration with Functional Maps, M Eisenberger, Z Lähner, D Cremers, CVPR, 2020

- SMPL650 Anonymous.

- SMPL650+Boostrapped (84k) Anonymous.

- SMPL650+Bootstrapped (192k) Anonymous.

- 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.

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