Abstract
Many applications use three-dimensional polygon meshes as geometric representation to achieve central tasks, such as 3D object retrieval and classification. However, implementing a deep learning approach dedicated to 3D meshes is a bit hard due to the complexity and irregularity of the mesh surface representation. In this paper, we propose a new geometric deep learning approach dedicated to representation learning, which applies convolutional operations on 3D meshes. In particular, we introduce a ring-unit convolutional operator that aggregates two graphs deduced from the mesh surface. Our network can learn highly discriminating features by avoiding complexity and irregularity problems.
We experimentally validated our approach on 3D shape classification tasks and the multi-domain protein shape retrieval challenge. A comparison with the state-of-the-art approaches proved the relevance of the learned features to the accuracy of 3D object classification and retrieval.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed, E., et al.: A survey on deep learning advances on different 3D data representations. arXiv preprint arXiv:1808.01462 (2018)
Aubry, M., Schlickewei, U., Cremers, D.: The wave kernel signature: a quantum mechanical approach to shape analysis. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV workshops), pp. 1626–1633. IEEE (2011)
Bronstein, M.M., Bruna, J., Cohen, T., Veličković, P.: Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478 (2021)
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Connolly, M.L.: Solvent-accessible surfaces of proteins and nucleic acids. Science 221(4612), 709–713 (1983)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural. Inf. Process. Syst. 29, 3844–3852 (2016)
Derr, T., Ma, Y., Tang, J.: Signed graph convolutional networks. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 929–934. IEEE (2018)
Du, J., Zhang, S., Wu, G., Moura, J.M., Kar, S.: Topology adaptive graph convolutional networks. arXiv preprint arXiv:1710.10370 (2017)
Fang, Y., et al.: 3D deep shape descriptor. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2319–2328 (2015)
Feng, Y., Feng, Y., You, H., Zhao, X., Gao, Y.: MeshNet: mesh neural network for 3D shape representation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8279–8286 (2019)
HajMohamed, H., Belaid, S., Naanaa, W., Romdhane, L.B.: Local commute-time guided MDS for 3D non-rigid object retrieval. Appl. Intell. 48(9), 2873–2883 (2018)
HajMohamed, H., Belaid, S., Naanaa, W., Romdhane, L.B.: Deep sparse dictionary-based representation for 3D non-rigid shape retrieval. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 1070–1077 (2021)
He, W., Jiang, Z., Zhang, C., Sainju, A.M.: CurvaNet: geometric deep learning based on directional curvature for 3D shape analysis. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2214–2224 (2020)
Kihara, D., Sael, L., Chikhi, R., Esquivel-Rodriguez, J.: Molecular surface representation using 3D Zernike descriptors for protein shape comparison and docking. Curr. Protein Pept. Sci. 12(6), 520–530 (2011)
Langenfeld, F., et al.: Shrec 2020: multi-domain protein shape retrieval challenge. Comput. Graph. 91, 189–198 (2020)
Masci, J., Boscaini, D., Bronstein, M., Vandergheynst, P.: Geodesic convolutional neural networks on Riemannian manifolds. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 37–45 (2015)
Milano, F., Loquercio, A., Rosinol, A., Scaramuzza, D., Carlone, L.: Primal-dual mesh convolutional neural networks. arXiv preprint arXiv:2010.12455 (2020)
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5115–5124 (2017)
Otu, E., Zwiggelaar, R., Hunter, D., Liu, Y.: Nonrigid 3D shape retrieval with HAPPS: a novel hybrid augmented point pair signature. In: 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 662–668. IEEE (2019)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. arXiv preprint arXiv:1706.02413 (2017)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)
Zhang, Y., Rabbat, M.: A graph-CNN for 3D point cloud classification. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6279–6283. IEEE (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Haj Mohamed, H., Belaid, S., Naanaa, W. (2022). RingNet: Geometric Deep Representation Learning for 3D Multi-domain Protein Shape Retrieval. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-16014-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16013-4
Online ISBN: 978-3-031-16014-1
eBook Packages: Computer ScienceComputer Science (R0)