From 2d to 3d Supervised Segmentation and Classification for Cultural Heritage Applications

Grilli, Eleonora; Dininno, Domenica; Petrucci, Giulio; Remondino, Fabio

doi:10.5194/isprs-archives-xlii-2-399-2018

Cited by 40 publications

(28 citation statements)

References 22 publications

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“…They subdivided the elements in architectural sub-elements creating many different orthoimages. Similar to this work is the idea of (Grilli et al, 2018), even if to avoid the creation of different orthoimages, they have produced the 3D model using textured UV maps. The authors use 2D segmentation of the texture of 3D models generated considering three archaeological case studies in Italy (Villa Adriana in Tivoli; Cavea walls of the Circus Maximus in Rome; Portico in Bologna).…”

Section: Related Workmentioning

confidence: 97%

Deep Learning for Semantic Segmentation of 3d Point Cloud

Malinverni

Pierdicca

Paolanti

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites. Cultural Heritage around the globe suffers from wars, natural disasters and human negligence. The importance of digital documentation is well recognized and there is an increasing pressure to document our heritage both nationally and internationally. For this reason, the three-dimensional scanning and modeling of sites and artifacts of cultural heritage have remarkably increased in recent years. The semantic segmentation of point clouds is an essential step of the entire pipeline; in fact, it allows to decompose complex architectures in single elements, which are then enriched with meaningful information within Building Information Modelling software. Notwithstanding, this step is very time consuming and completely entrusted on the manual work of domain experts, far from being automatized. This work describes a method to label and cluster automatically a point cloud based on a supervised Deep Learning approach, using a state-of-the-art Neural Network called PointNet++. Despite other methods are known, we have choose PointNet++ as it reached significant results for classifying and segmenting 3D point clouds. PointNet++ has been tested and improved, by training the network with annotated point clouds coming from a real survey and to evaluate how performance changes according to the input training data. It can result of great interest for the research community dealing with the point cloud semantic segmentation, since it makes public a labelled dataset of CH elements for further tests.</p>

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Section: Related Workmentioning

confidence: 97%

Deep Learning for Semantic Segmentation of 3d Point Cloud

Malinverni

Pierdicca

Paolanti

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…CNNs are also used by Yasser et al (2017) for visual categorization and to create a digital heritage search platform (ICARE) that allows users to archive digital heritage content and perform semantic queries over multimodal cultural heritage data archives. In some cases, the classification is performed for annotation and restoration purposes, and the information is transferred from 2D to 3D (Campanaro et al, 2016;Grilli et al, 2018). The web platform Aioli (www.aioli.cloud) allows a semi-automatic annotation of 3D heritage, where 2D mapping data are in realtime displayed onto a 3D model (Roussel et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Application of Machine and Deep Learning Strategies for the Classification of Heritage Point Clouds

Grilli

Özdemir

Remondino

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The use of heritage point cloud for documentation and dissemination purposes is nowadays increasing. The association of semantic information to 3D data by means of automated classification methods can help to characterize, describe and better interpret the object under study. In the last decades, machine learning methods have brought significant progress to classification procedures. However, the topic of cultural heritage has not been fully explored yet. This paper presents a research for the classification of heritage point clouds using different supervised learning approaches (Machine and Deep learning ones). The classification is aimed at automatically recognizing architectural components such as columns, facades or windows in large datasets. For each case study and employed classification method, different accuracy metrics are calculated and compared.

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“…Grilli et al (2017), in their review of point cloud segmentation and classification algorithms, highlight the following as suitable machine learning methods: Kmeans clustering, hierarchical clustering and mean shift. Recently, Grilli et al (2018) proposed a supervised machine learning method to classify 3D heritage models by segmenting 2D textures using traditional Random Forests (RF).…”

Section: Related Workmentioning

confidence: 99%

Between Images and Built Form: Automating the Recognition of Standardised Building Components Using Deep Learning

Pezzica

Schroeter

Prizeman

et al. 2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Building on the richness of recent contributions in the field, this paper presents a state-of-the-art CNN analysis method for automating the recognition of standardised building components in modern heritage buildings. At the turn of the twentieth century manufactured building components became widely advertised for specification by architects. Consequently, a form of standardisation across various typologies began to take place. During this era of rapid economic and industrialised growth, many forms of public building were erected. This paper seeks to demonstrate a method for informing the recognition of such elements using deep learning to recognise 'families' of elements across a range of buildings in order to retrieve and recognise their technical specifications from the contemporary trade literature. The method is illustrated through the case of Carnegie Public Libraries in the UK, which provides a unique but ubiquitous platform from which to explore the potential for the automated recognition of manufactured standard architectural components. The aim of enhancing this knowledge base is to use the degree to which these were standardised originally as a means to inform and so support their ongoing care but also that of many other contemporary buildings. Although these libraries are numerous, they are maintained at a local level and as such, their shared challenges for maintenance remain unknown to one another. Additionally, this paper presents a methodology to indirectly retrieve useful indicators and semantics, relating to emerging HBIM families, by applying deep learning to a varied range of architectural imagery.

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From 2d to 3d Supervised Segmentation and Classification for Cultural Heritage Applications

Cited by 40 publications

References 22 publications

Deep Learning for Semantic Segmentation of 3d Point Cloud

Deep Learning for Semantic Segmentation of 3d Point Cloud

Application of Machine and Deep Learning Strategies for the Classification of Heritage Point Clouds

Between Images and Built Form: Automating the Recognition of Standardised Building Components Using Deep Learning

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