B. M. Bronstein-m, Y. Lecun, . Szlam-a, and P. Vandergheynst, Geometric deep learning : going beyond euclidean data, IEEE Signal Processing Magazine, vol.34, pp.18-42, 2017.

. Cho-k, . Van-merriënboer-b, . Bahda-nau-d, and Y. Bengio, On the properties of neural machine translation : Encoder-decoder approaches, 2014.

M. Eitz, J. Hays, and A. M. , How do humans sketch objects ?, ACM Trans. Graph, vol.31, issue.4, pp.44-45, 2012.

Y. Gryaditskaya, M. Sypesteyn, J. W. Hofti-jzer, S. Pont, . Durand-f et al., Opensketch : A richly-annotated dataset of product design sketches
URL : https://hal.archives-ouvertes.fr/hal-02284134

. Grabli-s, E. Turquin, . Durand-f, and . X. Sil-lion-f, Programmable rendering of line drawing from 3d scenes, ACM Transactions on Graphics

H. D. Eck-d, A neural representation of sketch drawings, 2017.

. Huang-z, H. Fu, and . W. Lau-r, Data-driven segmentation and labeling of freehand sketches, ACM Transactions on Graphics (TOG), vol.33, p.175, 2014.

. Huang-z, H. Fu, and . W. Lau-r, Datadriven segmentation and labeling of freehand sketches, ACM Trans. Graph, vol.33, issue.6, pp.1-175, 2014.

. Iarussi-e, D. Bommes, and . Bousseau-a, Bendfields : Regularized curvature fields from rough concept sketches, ACM Transactions on Graphics, 2015.

P. Isola, . Zhu-j.-y, . Zhou-t, and A. A. Efros, Image-to-image translation with conditional adversarial networks, 2016.

L. Y. Bu-r, . Sun-m, . Wu-w, . Di-x, and . Chen-b, Pointcnn : Convolution on x-transformed points, Advances in Neural Information Processing Systems, pp.820-830, 2018.

L. L. Fu, H. , and T. , Fast sketch segmentation and labeling with deep learning, IEEE computer graphics and applications, vol.39, pp.38-51, 2018.

L. L. Zou, C. Zheng, Y. Su-q, H. Fu, and T. , Sketch-r2cnn : An attentive network for vector sketch recognition, 2018.

. Noris-g, D. S?kora, . Shamir-a, S. Coros, . Whited-b et al., Smart scribbles for sketch segmentation, Computer Graphics Forum, vol.31, pp.2516-2527, 2012.

C. R. Qi, H. Su, . Mo-k, and L. J. Guibas, Pointnet : Deep learning on point sets for 3d classification and segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.652-660, 2017.

. Ronneberger-o, . Fischer-p, and . Brox-t, Unet : Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.

C. H. Sudre, . Li-w, T. Vercauteren, S. Ourselin, and M. J. Cardoso, Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In Deep learning in medical image analysis and multimodal learning for clinical decision support, pp.240-248, 2017.

. Schaefer-s, . Mcphail-t, and J. Warren, Image deformation using moving least squares, ACM transactions on graphics (TOG) (2006), vol.25, pp.533-540

W. X. Chen-x and . Zha-z, Sketchpointnet : A compact network for robust sketch recognition, 25th IEEE International Conference on Image Processing, pp.2994-2998, 2018.

W. S. Liu-w, J. Wu, L. Cao, Q. Meng, and . J. Kennedy-p, Training deep neural networks on imbalanced data sets, 2016 international joint conference on neural networks (IJCNN), pp.4368-4374, 2016.

W. F. Lin-s, H. Wu, H. Li, . Wang-r, X. Luo et al., Spfusionnet : Sketch segmentation using multi-modal data fusion, 2019 IEEE International Conference on Multimedia and Expo (ICME), pp.1654-1659, 2019.

. Wu-x, Y. Qi, J. Liu, and Y. J. , Sketchsegnet : A rnn model for labeling sketch strokes, IEEE 28th International Workshop on Machine Learning for Signal Processing, pp.1-6, 2018.

W. Y. Sun, Y. Liu-z, . E. Sarma-s, M. M. Bronstein, and . Solomon-j.-m,