Multiclass semantic segmentation for digitisation of movable heritage using deep learning techniques

Giacomo Patrucco, Francesco Setragno


Digitisation processes of movable heritage are becoming increasingly popular to document the artworks stored in our museums. A growing number of strategies for the three-dimensional (3D) acquisition and modelling of these invaluable assets have been developed in the last few years. Their objective is to efficiently respond to this documentation need and contribute to deepening the knowledge of the masterpieces investigated constantly by researchers operating in many fieldworks. Nowadays, one of the most effective solutions is represented by the development of image-based techniques, usually connected to a Structure-from-Motion (SfM) photogrammetric approach. However, while images acquisition is relatively rapid, the processes connected to data processing are very time-consuming and require the operator’s substantial manual involvement. Developing deep learning-based strategies can be an effective solution to enhance the automatism level. In this research, which has been carried out in the framework of the digitisation of a wooden maquettes collection stored in the ‘Museo Egizio di Torino’, using a photogrammetric approach, an automatic masking strategy using deep learning techniques is proposed, to increase the level of automatism and therefore, optimise the photogrammetric pipeline. Starting from a manually annotated dataset, a neural network was trained to automatically perform a semantic classification to isolate the maquettes from the background. The proposed methodology allowed the researchers to obtain automatically segmented masks with a high degree of accuracy. The workflow is described (as regards acquisition strategies, dataset processing, and neural network training). In addition, the accuracy of the results is evaluated and discussed. Finally, the researchers proposed the possibility of performing a multiclass segmentation on the digital images to recognise different object categories in the images, as well as to define a semantic hierarchy to perform automatic classification of different elements in the acquired images.


  • In the framework of movable heritage digitisation processes, many procedures are very time-consuming, and they still require the operator’s substantial manual involvement.

  • This research proposes using deep learning techniques to enhance the automatism level in the generation of exclusion masks, improving the optimisation of the photogrammetric procedures.

  • Following this strategy, the possibility of performing a multiclass semantic segmentation (on the 2D images and, consequently, on the 3D point cloud) is also discussed, considering the accuracy of the obtainable results.


close-range photogrammetry; deep learning; semantic segmentation; automatic masking; movable heritage; cultural heritage documentation

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Adami, A., Balletti, C., Fassi, F., Fregonese, L., Guerra, F., Taffurelli, L., Vernier, P. (2015). The bust of Francesco II Gonzaga: From digital documentation to 3D printing. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-5/W3, 9-15.

Badrinarayanan, V., Kendall, A., Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481-2495.

Balletti, C., Ballarin, M., & Guerra, F. (2017). 3D printing: state of the art and future perspectives. Journal of Cultural Heritage, 26,172-182.

Balletti, C., & Ballarin, M. (2019). An application of integrated 3D technologies for replicas in Cultural Heritage. International Journal of Geo-Information, 8(6), 285.

Barbieri, L., Bruno, F., & Muzzupappa, M. (2018). User-centered design of a virtual reality exhibit for archaeological museums. International Journal on Interactive Design and Manufacturing (IJIDeM), 12, 561-571.

Caruana, R., Lawrence, S., & Giles, C. L. (2001). Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. Advances in Neural Information Processing Systems (pp. 402-408).

Cermelli, F., Mancini, M., Bulo, S. R., Ricci, E., & Caputo, B. (2020). Modeling the background for incremental learning in semantic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9233-9242.

Condorelli, F., Rinaudo, F., Salvadore, F., & Tagliaventi, S. (2020). A neural network approach to detecting lost heritage in historical video. International Journal of Geo-Information, 9(5), 297.

Chiabrando, F., Sammartano, G., Spanò, A., & Spreafico, A. (2019). Hybrid 3D models: When Geomatics innovations meet extensive built heritage complexes. International Journal of Geo-Information, 8(3), 124.

Dall’Asta, E., Bruno, N., Bigliardi, G., Zerbi, A., & Roncella, R.

(2016). Photogrammetric techniques for promotion of archaeological Heritage: the Archaeological Museum of Parma (Italy). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B5, 243-250.

Felicetti, A., Paolanti, M., Zingaretti, P., Pierdicca, R., & Malinverni, E. S. (2020). Mo.Se.: Mosaic image segmentation based on deep cascading learning. Virtual Archaeology Review, 12(24), 25-38.

Fiorucci, M., Khoroshiltseva, M., Pontil, M., Traviglia, A., Del Bue, A., & James, S. (2020). Machine Learning for Cultural Heritage: A Survey. Pattern Recognition Letters, 133, 102-108.

Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., & Garcia-Rodriguez, J. (2017). A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing, 70, 41-65.

George, D., Xie, X., & Tam, G. K. (2018). 3D mesh segmentation via multi-branch 1D convolutional neural networks. Graphical Models, 96, 1-10.

Giuffrida, D., Mollica Nardo, V., Giacobello, F., Adinolfi, O., Mastelloni, M. A., Toscano, G., & Ponterio, R. S. (2019). Combined 3D surveying and Raman Spectroscopy Techniques on artifacts preserved at Archaeological Musem of Lipari. Heritage, 2(3), 2017-2027.

Grilli, E., Farella, E. M., Torresani, A., & Remondino, F. (2019). Geometric features analysis for the classification of Cultural Heritage point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W15, 541-548.

Grilli, E., Özdemir, E., & Remondino, F. (2019). Application of machine and deep learning strategies for the classification of Heritage point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W18, 447-454.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Gu, J., Wang, Z., Kuen, J., Ma., L., Shahroudy, A., Shuai, B., & Chen., T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377.

Guidi, G., Malik, U. S., Frischer, B., Barandoni, C., & Paolucci, F. (2017). The Indiana University-Uffizi project: Metrological challenges and workflow for massive 3D digitization of sculptures. 23rd International Conference on Virtual System & Multimedia (VSMM), 1-8.

He, T., Shen, C., Tian, Z., Gong, D., Sun, C., & Yan, Y. (2019). Knowledge adaptation for efficient semantic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 578-587.

Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., & Bengio, Y. (2017). The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 11-19).

Kersten, T. P., Tschirschwitz, F., & Deggim, S. (2017). Development of a virtual museum including a 4D presentation of building history in Virtual Reality. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W3, 361-367.

Knyaz, A. V., Kniaz, V. V., Remondino, F., Zheltov, S. Y., & Gruen, A. (2020). 3D reconstruction of a complex grid structure combining UAS images and deep learning. Remote Sensing, 12(19), 3128.

Lin, P., Sun, P., Cheng, G., Xie, S., Li, X., & Shi, J. (2020). Graph-guided architecture search for real-time semantic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4203-4212.

Llamas, J., Lerones, P. M., Medina, R., Zalama, E., & Gómez-García-Bermejo, J. (2017). Classification of architectural heritage images using deep learning techniques. Applied Science, 7(10), 992.

Lo Turco, M., Piumatti, P., Rinaudo, F., Tamborrino, R., & González-Aguilera, D., (2018). B.A.C.K. TO T.H.E. F.U.T.U.RE. − BIM acquisition as cultural key to transfer heritage of ancient Egypt for many uses to many users replayed. In S. Bertocci (Ed.), Programmi Multidisciplinari Per L’internazionalizzazione Della Ricerca. Patrimonio Culturale, Architettura e Paesaggio (pp. 107-109). DIDA Press.

Lo Turco, M., Piumatti, P., Rinaudo, F., Calvano, M., Spreafico, A., & Patrucco, G. (2018). The digitisation of museum collections for research, management and enhancement of tangible and intangible heritage. 3rd Digital Heritage International Congress (DigitalHERITAGE) held jointly with 24th International Conference on Virtual Systems & Multimedia (VSMM 2018), San Francisco, CA, USA.

Mafrici, N., & Giovannini, E. C. (2020). Digitalizing data: From the historical research to data modelling for a (digital) collection documentation. In M. Lo Turco, E. C. Giovannini, , & N. Mafrici (Eds.), Digital & Documentation. Digital Strategies for Cultural Heritage (Vol. 2, pp. 38-51). Pavia University Press.

Malik, U. S., Guidi, G. (2018). Massive 3D digitization of sculptures: Methodological approaches for improving efficiency. IOP Conference Series: Material Science and Engineering, 364.

Minto, S., & Remondino, F. (2014). Online access and sharing of reality-based 3D models. SCIRES-IT-SCIentific RESearch and Information Technology, 4(2), 17-28.

Patrucco, G., Chiabrando, F., Dondi, P, & Malagodi, M. (2018). Image and range-based 3D acquisition and modeling of popular musical instruments. Proceedings from the Document Academy, 5(2), 9.

Patrucco, G., Rinaudo, F., & Spreafico, A. (2019). A new handheld scanner for 3D survey of small artifacts: The Stonex F6. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W15, 895-901.

Pierdicca, R., Paolanti, M., Matrone, F., Martini, M., Morbidoni, C., Malinverni, E. S., Frontoni, E., & Lingua, A. M. (2020). Point cloud semantic segmentation using a deep learning framework for Cultural Heritage. Remote Sensing, 12(6), 1005.

Salvador-García, E., Viñals, M. J., & García-Valldecabres, J. L. (2020). Potential of HBIM to improve the efficiency of visitor flow management in Heritage sites. Towards smart heritage management. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIV-M-1-2020, 451-456.

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1-48.

Stathopoulou, E. K., & Remondino, F. (2019). Semantic photogrammetry: Boosting image-based 3D reconstruction with semantic labeling. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W9, 685-690.

UNESCO. (1979). Recommendation for the Protection of Movable Cultural Property, Records of the General Conference, 20th Session, I: Resolutions. Paris: UNESCO.

Vargas, R., Mosavi, A., & Ruiz, R. (2018). Deep learning: A review. Advances in Intelligent Systems and Computing, 29(8), 232-244.

Yazan, E., & Talu, M. F. (2017). Comparison of the stochastic gradient descent based optimization techniques. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 1-5.

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