, MIT CSAIL Textured Models Database, 2008.

G. Vector, , pp.2020-2023, 2014.

, Publish & find 3D models online, 2020.

S. Biasotti, A. Cerri, M. Aono, A. B. Hamza, V. Garro et al., , p.1

D. Giorgi, A. Godil, C. Li, and C. Sanada, Retrieval and classification 2 methods for textured 3D models: A comparative study. The Visual Com-3

, puter, vol.32, issue.2, pp.217-241, 2016.

S. Biasotti, E. Thompson, M. Aono, A. B. Hamza, and B. Bustos, , p.5

S. Dong, B. Du, A. Fehri, H. Li, and F. A. Limberger, Retrieval of 6 surfaces with similar relief patterns: Shrec'17 track

, on 3D Object Retrieval, 3Dor '17, vol.8, pp.95-103

S. Biasotti, E. Thompson, L. Barthe, S. Berretti, and A. Giachetti, , p.10

T. Lejemble, N. Mellado, K. Moustakas, I. Manolas, D. Dimou et al., , p.11

S. Velasco-forero, N. Werghi, M. Polig, G. Sorrentino, and S. Hermon, 12 Recognition of Geometric Patterns Over 3D Models, 11 th EG Workshop 13 on 3D Object Retrieval, p.14, 2018.

A. Cerri, S. Biasotti, M. Abdelrahman, J. Angulo, K. Berger et al., , p.15

M. El-melegy, A. Farag, F. Lefebvre, A. Giachetti, H. Guermoud et al., , p.16

S. Velasco-forero, J. Vigouroux, C. Xu, and J. Zhang, SHREC'13 17 Track: Retrieval on Textured 3D Models. In 6 th EG Workshop on 3D 18 Object Retrieval, p.19, 2013.

G. Chechik, V. Sharma, U. Shalit, and S. Bengio, Large scale online learn-20 ing of image similarity through ranking, Journal of Machine Learning 21 Research, vol.11, issue.36, p.22, 2010.

S. Chopra, R. Hadsell, and Y. Lecun, Learning a similarity metric dis-23 criminatively, with application to face verification, Proceedings of the 24 2005 IEEE Computer Society Conference on Computer Vision and Pattern, p.25

, CVPR '05, vol.1, p.27, 2005.

M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, Describ-28 ing textures in the wild, Proceedings of the 2014 IEEE Conference on, p.29

, Computer Vision and Pattern Recognition, CVPR '14, vol.30, pp.3606-3613

D. C. Washington and . Usa, , p.31, 2014.

M. Ester, H. Kriegel, J. Sander, and X. Xu, A density-based algorithm 32 for discovering clusters in large spatial databases with noise, Proceedings 33 of the Second International Conference on Knowledge Discovery and Data, p.34

. Mining, , p.35, 1996.

J. H. Friedman, J. L. Bentley, and R. A. Finkel, An algorithm for finding 36 best matches in logarithmic expected time, ACM Trans. Math. Softw, vol.37, issue.3, pp.209-226, 1938.

A. Giachetti, Effective characterization of relief patterns, Computer Graph-39 ics Forum, vol.37, issue.5, p.40, 2018.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image 1 recognition, 2015.

G. Huang, Z. Liu, G. Pleiss, L. Van-der-maaten, and K. Weinberger,

, Convolutional networks with dense connectivity. IEEE Transactions on 4 Pattern Analysis and Machine Intelligence, 2019.

H. Jégou, F. Perronnin, M. Douze, J. Sánchez, P. Pérez et al.,

, Aggregating local image descriptors into compact codes, IEEE Transac-7 tions on Pattern Analysis and Machine Intelligence, 2011.

N. Kanopoulos, N. Vasanthavada, and R. L. Baker, Design of an image 9 edge detection filter using the sobel operator, IEEE Journal of solid-state, vol.10, issue.2, pp.358-367, 1988.

W. Mathworld,

. Dotproduct, , pp.2019-2029

E. Thompson, G. Arvanitis, K. Moustakas, N. Hoang-xuan, and E. R. ,

M. Nguyen, T. Tran, L. Lejemble, N. Barthe, C. Mellado et al., , p.15

S. Biasotti and B. Falcidieno, Feature Curve Extraction on Triangle, vol.16

. Meshes, 12 th EG Workshop on 3D Object Retrieval, pp.85-92, 2019.

E. , M. Thompson, and S. Biasotti, Description and retrieval of geomet-19 ric patterns on surface meshes using an edge-based lbp approach, vol.82, pp.1-15, 2018.

E. , M. Thompson, and S. Biasotti, Edge-based LBP Description of

T. Ojala, M. Pietikäinen, and D. Harwood, A comparative study of tex-1

, Recognition, vol.29, pp.51-59, 1996.

A. Othmani, F. Torkhani, and J. Favreau, , 2019.

A. Othmani, L. F. Voon, C. Stolz, and A. Piboule, Single tree species 6 classification from terrestrial laser scanning data for forest inventory

, tern Recognition Letters, vol.34, pp.2144-2150, 2013.

E. Otu, R. Zwiggelaar, D. Hunter, and Y. Liu,

, trieval with happs: A novel hybrid augmented point pair signature, 10 2019 International Conference on Computational Science and Computa-11 tional Intelligence (CSCI), p.12, 2019.

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, Pointnet: Deep learning on 13 point sets for 3d classification and segmentation, Proc. Computer Vision 14 and Pattern Recognition (CVPR), p.15, 2017.

C. J. Rijsbergen, Information Retrieval. Butterworth-Heinemann, p.17, 1979.

P. Shilane, P. Min, M. Kazhdan, and T. Funkhouser, The Princeton shape 18 benchmark, Shape Modeling International, pp.167-178, 2004.

K. Simonyan, O. M. Parkhi, A. Vedaldi, and A. Zisserman, Fisher vector 20 faces in the wild, BMVC, vol.2, 2013.

K. Simonyan and A. Zisserman, Very deep convolutional networks for 22 large-scale image recognition. CoRR, abs/1409.1556, 2014.

K. Simonyan and A. Zisserman, Very deep convolutional networks for 24 large-scale image recognition

I. Sipiran, J. Loko, B. Bustos, and T. Skopal, Scalable 3d shape retrieval 26 using local features and the signature quadratic form distance. The Visual 27 Computer, vol.33, p.28, 2017.

M. Tan, Q. V. Le, and . Efficientnet, Rethinking model scaling for convolu-29 tional neural networks. CoRR, abs/1905.11946, p.30, 2019.

F. Tombari, S. Salti, and L. Stefano, Unique signatures of histograms for 31 local surface description, Proceedings of the 11th European Conference 32 on Computer Vision Conference on Computer Vision: Part III, ECCV'10, vol.33, pp.356-369, 2010.

C. Tortorici, N. Werghi, and S. Berretti, Extending LBP and Convolution-35 Like Operations on the Mesh, IEEE Int. Conf. on Image Processing 36 (ICIP), p.37, 2019.

E. Wahl, U. Hillenbrand, and G. Hirzinger,

N. Werghi, C. Tortorici, S. Berretti, and A. D. Bimbo, Representing 11 3D texture on mesh manifolds for retrieval and recognition applications, 12 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), vol.13, pp.2019-2029, 2014.

C. , Sample elimination for generating poisson disk sample sets

, Computer Graphics Forum, vol.34, 2015.

M. Zeppelzauer, G. Poier, M. Seidl, C. Reinbacher, S. Schulter et al., Bre-19 iteneder, and H. Bischof. Interactive 3D segmentation of rock-art by en-20 hanced depth maps and gradient preserving regularization, J. Comput

, Cult. Herit, vol.9, issue.4, 2016.

Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang et al., 3d shapenets: A deep representation for volumetric shapes, 2015 IEEE Conference on Computer Vision and Pattern Recognition 25 (CVPR), vol.23, pp.1912-1920, 2015.