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Erschienen in:
A Multi-layered Approach for Tailored Black-Box Explanations Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Machines Learning for Mixed Reality

The Milan Cathedral from Survey to Holograms

verfasst von : Simone Teruggi, Francesco Fassi

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

In recent years, a complete 3D mapping of the Cultural Heritage (CH) has become fundamental before every other action could follow. Different survey techniques outputs could be combined in a 3D point cloud, completely describing the geometry of even the most complex object. These data very rich in metric quality can be used to extract 2D technical elaborations and advanced 3D representations to support conservation interventions and maintenance planning.
The case of Milan Cathedral is outstanding. In the last 12 years, a multi-technique omni-comprehensive survey has been carried out to extract the technical representations that are used by the Veneranda Fabbrica (VF) del Duomo di Milano to plan its maintenance and conservation activities.
Nevertheless, point cloud data lack structured information such as semantics and hierarchy among parts, fundamentals for 3D model interaction and database (DB) retrieval. In this context, the introduction of point cloud classification methods could improve data usage, model definition and analysis.
In this paper, a Multi-level Multi-resolution (MLMR) classification approach is presented and tested on the large dataset of Milan Cathedral. The 3D point model, so structured, for the first time, is used directly in a Mixed Reality (MR) environment to develop an application that could benefit professional works, allowing to use 3D survey data on-site, supporting VF activities.

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Vorheriges Kapitel Attention-Based Multi-modal Emotion Recognition from Art
Nächstes Kapitel From Fully Supervised to Blind Digital Anastylosis on DAFNE Dataset
Fußnoten
1
Conducted by Politecnico di Milano, ABC department, Group 3Dsurvey.
 
2
“The “Veneranda Fabbrica del Duomo di Milano” is an ecclesiastical body endowed with legal personality by ancient statutory determination with the purpose of worship and religion, which excludes all profit-making activities. Its earliest regulations were issued on 16 October 1387 at the behest of Gian Galeazzo Visconti.” [1]
 
4
The HoloLens 2 device works on the possibility to track hands movements with its cameras, when it recognizes that an hand is present in the rendering area it will project a dashed line in the direction to which the hand is pointing.
 
5
Air tap interaction consists in touching the index finger with the thumb of the same hand and immediately releasing them while the HoloLens 2 is tracking their position.
 
Literatur
1.
2.
Fassi, F., Achille, C., Fregonese, L.: Surveying and modelling the main spire of milan cathedral using multiple data sources. Photogram. Rec. 26, 462–487 (2011)CrossRef
3.
Fassi, F., Achille, C., Mandelli, A., Rechichi, F., Parri, S.: A new idea of BIM system for visualization, WEB sharing and using huge complex 3D models for facility management. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-5/W4, pp. 359–366 (2015)
4.
Rechichi, F., Mandelli, A., Achille, C., Fassi, F.: Sharing high-resolution modules and information on the WEB: the WEB module of BIM3DSG system. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLI-B5, pp. 703–710 (2016)
5.
Fassi, F., Parri, S.: Complex architecture in 3D: from survey to web. Int. J. Heritage Digit. Era 1(3), 379–398 (2012)CrossRef
6.
Achille, C., Fassi, F., Mandelli, A., Perfetti, L., Rechichi, F., Teruggi, S.: From a traditional to a digital site: 2008–2019. The history of Milan Cathedral surveys. In: Daniotti, B., Gianinetto, M., Della Torre, S. (eds.) Digital Transformation of the Design, Construction and Management Processes of the Built Environment. RD, pp. 331–341. Springer, Cham (2020). https://​doi.​org/​10.​1007/​978-3-030-33570-0_​30CrossRef
7.
Perfetti, L., Fassi, F., Gulsan, H.: Generation of gigapixel orthophoto for the maintenance of complex buildings. Challenges and lesson learnt. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W9, pp. 605–614 (2019)
8.
Mandelli, A., Fassi, F., Perfetti, L., Polari, C.: Testing different survey techniques to model architectonic narrow spaces. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W5, pp. 505–511 (2017)
9.
Teruggi, S., Grilli, E., Russo, M., Fassi, F., Remondino, F.: A hierarchical machine learning approach for multi-level and multi-resolution 3D point cloud classification. Remote Sens. 12(16), 2598 (2020)CrossRef
10.
Reboli, D., Pučko, Z., Babič, N.Č, Bizjak, M., Mongus, D.: Point cloud quality requirements for scan-vs-BIM based automated construction progress monitoring. Autom. Constr. 84, 323–334 (2017)CrossRef
11.
Son, H., Kim, C.: Semantic as-built 3D modelling of structural elements of buildings based in local concavity and convexity. Adv. Eng. Inform. 34, 114–124 (2017)CrossRef
12.
Bassier, M., Yousefzadeh, M., Vergauwen, M.: Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM. J. Inf. Technol. Constr. (ITcon) 25(11), 173–192 (2020)
13.
Apollonio, F.I., et al.: A 3D-centered information system for the documentation of a complex restoration intervention. J. Cult. Heritage 29, 89–99 (2018)CrossRef
14.
Croce, V., Caroti, G., De Luca, L., Piemonte, A., Véron, P.: Semantic annotation of heritage models: 2D/3D approaches and future research challenges. In: The International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, vol. 43, pp. 829–836 (2020)
15.
Sánchez-Aparicio, L.J., Del Pozo, S., Ramos, L.F., Arce, A., Fernandes, F.M.: Heritage site preservation with combined radiometric and geometric analysis of TLS data. Automation in Construction 85, 24–39 (2018)CrossRef
16.
Valero, E., Bosché, F., Forster, A.: Automatic segmentation of 3D point cloud of rubble masonry walls and its application to building surveying Repair and Maintenance. Autom.Constr. 96, 29–39 (2018)CrossRef
17.
Roussel, R., Bagnéris, M., De Luca, L., Bomblet, P.: A digital diagnosis for the <<Autumn>> statue (Marseille, France): photogrammetry, digital cartography and construction of a thesaurus. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, vol. XLII-2/W15, pp. 1039–1046 (2019)
18.
Mizoguchi, T., et al.: Quantitative scaling evaluation of concrete structures based on terrestrial laser scanning. Autom. Constr. 35, 263–274 (2013)CrossRef
19.
Kashani, A.G., Graettinger, A.J.: Cluster-based roof covering damage detection in ground-based lidar data. Autom. Constr. 58, 19–27 (2015)CrossRef
20.
Matrone, F., et al.: A benchmark for large-scale heritage point cloud semantic segmentation. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, vol. XLIII-B2, pp. 1419–1426 (2020)
21.
Murtiyoso, A., Grussenmeyer, P.: Virtual disassembling of historical edifices: experiments and assessments of an automatic approach for classifying multi-scalar point clouds into architectural elements. Sensors 20(8), 2161 (2020)CrossRef
22.
Pierdicca, R., et al.: Point cloud semantic segmentation using deep learning framework for cultural heritage. Remote Sens. 12(6), 1005 (2020)CrossRef
23.
Grilli, E., Dininno, D., Marsicano, L., Petrucci, G., Remondino, F.: Supervised segmentation of 3D cultural heritage. In 2018 3rd Digital Heritage International Congress (DigitalHERITAGE) Held Jointly with 2018 24th International Conference on Virtual Systems & Multimedia (VSMM 2018), San Francisco, CA, USA, pp. 1–8. IEEE (2018)
24.
Grilli, E., M. Farella, E., Torresani, A., Remondino, F.: geometric features analysis for the classification of cultural heritage point clouds. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, vol. XLII-2/W15, pp. 541–548 (2019)
25.
Grilli, E., Remondino, F.: Machine learning generalization across different 3D architectural heritage. ISPRS Int. J. Geo-Inf. 9(6), 379 (2020)CrossRef
26.
Matrone, F., Grilli, E., Martini, M., Paolanti, M., Pierdicca, R., Remondino, F.: Comparing machine and deep learning methods for large 3D heritage semantic segmentation. ISPRS Int. J. Geo-Inf. 9(9), 535 (2020)CrossRef
27.
Blomley, R., Weinmann, M., Leitloff, J., Jutzi, B.: Shape distribution features for point cloud analysis – a geometric histogram approach on multiple scales. In: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 2, no. 3, p. 9 (2014)
28.
29.
30.
Milgram, P., Takemura, H., Utsumi, A., Kishino, F.: Augmented reality: a class of displays on the reality-virtuality continuum. Telemanipulator Telepresence Technol. 2351, 282–292 (1995)CrossRef
31.
De Pace, F., Manuri, F., Sanna, A.: Augmented reality in Industry 4.0. Am. J. Comput. Sci. Inf. Technol. 06(01), 1–7 (2018)
32.
Fraga-Lamas, P., Fernandez-Carames, T. M., Blanco-Nova, O., Vilar-Montesinos, M. A.: A review of industrial augmented reality systems for the Industry 4.0 Shipyard. IEEE Access 6, 13358–13375 (2018)
33.
Desselle, M.R., Brown, R.A., James, A.R., Midwinter, M.J., Powel, S.K., Woodruff, M.A.: Augmented and virtual reality in surgery. Comput. Sci. Eng. 22(3), 18–26 (2020)CrossRef
34.
Blanco-Pons, S., Carrion-Ruiz, B., Duong, M., Chartrand, J., Fai, S., Luis Lerma, J.: Augmented reality markerless multi-image outdoor tracking system for the historical buildings on parliament hill. Sustainability 11(16), 4268 (2019)CrossRef
35.
36.
37.
Metadaten
Titel
Machines Learning for Mixed Reality
verfasst von
Simone Teruggi
Francesco Fassi
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68795-3

Electronic ISBN: 978-3-030-68796-0

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68796-0_44