Abstract
Textures in 3D meshes represent intrinsic surface properties and are essential for numerous applications, such as retrieval, segmentation, and classification. The computer vision approaches commonly used in the cultural heritage domain are retrieval and classification. Mainly, these two approaches consider an input 3D mesh as a whole, derive features of global shape, and use them to classify or retrieve. In contrast, texture classification requires objects to be classified or retrieved based on their textures, not their shapes. Most existing techniques convert 3D meshes to other domains, while only a few are applied directly to 3D mesh. The objective is to develop an algorithm that captures the surface variations induced by textures. This paper proposes an approach for texture classification directly applied to the 3D mesh to classify the surface into texture and non-texture regions. We employ a hybrid method in which classical features describe each facet locally, and these features are then fed into a deep transformer for binary classification. The proposed technique has been validated using SHREC’18 texture patterns, and the results demonstrate the proposed approach’s effectiveness.
Supported by a research fund from Khalifa University, Ref: CIRA-2019-047.
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References
Andreetto, M., Brusco, N., Cortelazzo, G.M.: Automatic 3D modeling of textured cultural heritage objects. IEEE Trans. Image Process. 13(3), 354–369 (2004)
Biasotti, S., et al.: Shrec’17 track: retrieval of surfaces with similar relief patterns. In: 10th Eurographics Workshop on 3D Object Retrieval (2017)
Biasotti, S., et al.: Shrec’18 track: recognition of geometric patterns over 3D models. In: Eurographics Workshop on 3D Object Retrieval, vol. 2, pp. 71–77 (2018)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Ganapathi, I.I., Javed, S., Fisher, R.B., Werghi, N.: Graph based texture pattern classification. In: 2022 8th International Conference on Virtual Reality (ICVR), pp. 363–369 (2022)
Giachetti, A.: Effective characterization of relief patterns. In: Computer Graphics Forum, vol. 37, pp. 83–92 (2018)
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D.: Meshcnn: a network with an edge. ACM Trans. Graph. 38(4), 1–12 (2019)
Hu, S., Li, Z., Wang, S., Ai, M., Hu, Q.: A texture selection approach for cultural artifact 3D reconstruction considering both geometry and radiation quality. Remote Sens. 12(16), 2521 (2020)
Li, W., et al.: Shrec 2020 track: extended monocular image based 3D model retrieval (2020)
Limberger, F.A., Wilson, R.C.: Feature encoding of spectral signatures for 3D non-rigid shape retrieval. In: BMVC, pp. 56–61 (2015)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Masoumi, M., Li, C., Hamza, A.B.: A spectral graph wavelet approach for nonrigid 3D shape retrieval. Pattern Recogn. Lett. 83, 339–348 (2016)
Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of 12th International Conference on Pattern Recognition, vol. 1, pp. 582–585 (1994)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8026–8037 (2019)
Pieraccini, M., Guidi, G., Atzeni, C.: 3D digitizing of cultural heritage. J. Cult. Heritage 2(1), 63–70 (2001)
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)
Sipiran, I., et al.: Shrec 2021: retrieval of cultural heritage objects. Comput. Graph. 100, 1–20 (2021)
Sipiran, I., Lokoc, J., Bustos, B., Skopal, T.: Scalable 3D shape retrieval using local features and the signature quadratic form distance. Vis. Comput. 33(12), 1571–1585 (2017)
Thompson, E.M., Biasotti, S.: Description and retrieval of geometric patterns on surface meshes using an edge-based LBP approach. Pattern Recogn. 82, 1–15 (2018)
Tortorici, C., Berretti, S., Obeid, A., Werghi, N.: Convolution operations for relief-pattern retrieval, segmentation and classification on mesh manifolds. Pattern Recogn. Lett. 142, 32–38 (2021)
Werghi, N., Berretti, S., Del Bimbo, A.: The mesh-lbp: a framework for extracting local binary patterns from discrete manifolds. IEEE Trans. Image Process. 24(1), 220–235 (2014)
Werghi, N., Tortorici, C., Berretti, S., Del Bimbo, A.: Local binary patterns on triangular meshes: Concept and applications. Comput. Vis. Image Understand. 139, 161–177 (2015)
Werghi, N., Tortorici, C., Berretti, S., Del Bimbo, A.: Representing 3D texture on mesh manifolds for retrieval and recognition applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2521–2530 (2015)
Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.: Surface feature detection and description with applications to mesh matching. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 373–380 (2009)
Zhou, D., et al.: Deepvit: towards deeper vision transformer. arXiv preprint arXiv:2103.11886 (2021)
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Ganapathi, I.I., Javed, S., Hassan, T., Werghi, N. (2023). Detecting 3D Texture on Cultural Heritage Artifacts. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_1
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