The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
3D-CODED: 3D Correspondences by Deep Deformation
Open accessAbstract
We present a new deep learning approach for matching deformable shapes by introducing Shape Deformation Networks which jointly encode 3D shapes and correspondences. This is achieved by factoring the surface representation into (i) a template, that parameterizes the surface, and (ii) a learnt global feature vector that parameterizes the transformation of the template into the input surface. By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template. We show that these correspondences can be improved by an additional step which improves the shape feature by minimizing the Chamfer distance between the input and transformed template. We demonstrate that our simple approach improves on state-of-the-art results on the difficult FAUST-inter challenge, with an average correspondence error of 2.88 cm. We show, on the TOSCA dataset, that our method is robust to many types of perturbations, and generalizes to non-human shapes. This robustness allows it to perform well on real unclean, meshes from the SCAPE dataset.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R. Q., Su, H., Kaichun, M., & Guibas, L. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.16 · Cited by 9,237
3D ShapeNets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298801 · Cited by 4,550
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00593 · Cited by 2,817
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Fan, H., Su, H., & Guibas, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.264 · Cited by 2,402
Discrete Differential-Geometry Operators for Triangulated 2-Manifolds
Meyer, M., Desbrun, M., Schröder, P., & Barr, A. H. (2003) · Mathematics and Visualization · doi:10.1007/978-3-662-05105-4_2 · Cited by 1,889
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., & Bronstein, M. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.576 · Cited by 1,820
SCAPE: shape completion and animation of people
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073207 · Cited by 1,489
Unique Signatures of Histograms for Local Surface Description
Tombari, F., Salti, S., & Di Stefano, L. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15558-1_26 · Cited by 1,480
Cited by
End-to-End Human Pose and Mesh Reconstruction with Transformers
Lin, K., Wang, L., & Liu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00199 · Cited by 740
Learning Joint Reconstruction of Hands and Manipulated Objects
Hasson, Y., Varol, G., Tzionas, D., Kalevatykh, I., Black, M. J., Laptev, I., & Schmid, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01208 · Cited by 513
Estimating somatotype from a single-camera 3D body scanning system
Chiu, C., Ciems, R., Thelwell, M., Bullas, A., & Choppin, S. (2022) · European Journal of Sport Science · doi:10.1080/17461391.2021.1921041 · Cited by 17
One Model to Rig Them All: Diverse Skeleton Rigging with UniRig
Zhang, J.-P., Pu, C.-F., Guo, M.-H., Cao, Y.-P., & Hu, S.-M. (2025) · ACM Transactions on Graphics · doi:10.1145/3730930 · Cited by 12
“Another approach is to drive entirely through neural networks, and some research[Groueix et al. 2018; Yu et al. 2025] efforts have also explored this.”
Metadata
- Authors
- Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., & Aubry, M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 235-251
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2883758202
- Cited by
- 293
- References
- 53
- FWCI
- 48.42
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: 3D-CODED: 3D Correspondences by Deep Deformation. https://app.cochat.ai/papers/10.1007/978-3-030-01216-8_15
- The paper
- Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., & Aubry, M. (2018). 3D-CODED: 3D Correspondences by Deep Deformation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01216-8_15
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
