A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

Teruggi, Simone; Grilli, Eleonora; Russo, Michele; Fassi, Francesco; Remondino, Fabio

doi:10.3390/rs12162598

Cited by 59 publications

(38 citation statements)

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“…Further developments of this approach led to test the algorithm also for multi-level and multi-scale semantic segmentation (Teruggi et al, 2020). With the same goal, (Murtiyoso and Grussenmeyer, 2020) presented an algorithmic approach in the form of a toolbox that supports the manual segmentation of large point clouds, including several semi-automated pipelines.…”

Section: Previous Workmentioning

confidence: 99%

Connecting Geometry and Semantics via Artificial Intelligence: From 3d Classification of Heritage Data to H-Bim Representations

Croce

Bevilacqua

Crepaldi

et al. 2021

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

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Abstract. Cultural heritage information systems, such as H-BIM, are becoming more and more widespread today, thanks to their potential to bring together, around a 3D representation, the wealth of knowledge related to a given object of study. However, the reconstruction of such tools starting from 3D architectural surveying is still largely deemed as a lengthy and time-consuming process, with inherent complexities related to managing and interpreting unstructured and unorganized data derived, e.g., from laser scanning or photogrammetry. Tackling this issue and starting from reality-based surveying, the purpose of this paper is to semi-automatically reconstruct parametric representations for H-BIM-related uses, by means of the most recent 3D data classification techniques that exploit Artificial Intelligence (AI). The presented methodology consists of a first semantic segmentation phase, aiming at the automatic recognition through AI of architectural elements of historic buildings within points clouds; a Random Forest classifier is used for the classification task, evaluating each time the performance of the predictive model. At a second stage, visual programming techniques are applied to the reconstruction of a conceptual mock-up of each detected element and to the subsequent propagation of the 3D information to other objects with similar characteristics. The resulting parametric model can be used for heritage preservation and dissemination purposes, as common practices implemented in modern H-BIM documentation systems. The methodology is tailored to representative case studies related to the typology of the medieval cloister and scattered over the Tuscan territory.

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Section: Previous Workmentioning

confidence: 99%

Connecting Geometry and Semantics via Artificial Intelligence: From 3d Classification of Heritage Data to H-Bim Representations

Croce

Bevilacqua

Crepaldi

et al. 2021

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

View full text Add to dashboard Cite

show abstract

“…The standard object libraries do not fully satisfy the needs of the representation of the existing building, especially when the scale of the drawing shows a high level of detail. For this reason, the research explores different strategies for recognizing, modelling and managing data, from the creation of a library of parametric architectural elements (Dore, & Murphy, 2013) to the segmentation of raw data (Teruggi, Grilli, Russo, Fassi & Remondino, 2020). The goal is always to 'recognise' the architectural elements and the construction of the building so that it can be effectively managed by the HBIM process.…”

Section: Digital and Theoretical Competenciesmentioning

confidence: 99%

Enhancing and Managing Data and Digital Competencies for Architecture Teaching and Training in the Field of Protection of Heritage

Fioretto

Lombardini

Achille

et al. 2021

Proceedings ARQUEOLÓGICA 2.0 - 9th International Congress &Amp; 3rd GEORES - GEOmatics and pREServation

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Given the growing relevance, at national and international levels, of restoration and conservation interventions on existing buildings, the Universities have developed degree courses with specific addresses in “Conservation of Architectural and Environmental Heritage”. The students that attend this course become a graduate with specific, extensive, and updated skills in the field of knowledge, protection, conservation, reuse, and enhancement of architectural and environmental heritage. The complexity of the intervention is faced through the contribution of the various disciplines that contribute to the training of the architect, at the same time they studied modern instruments and tools for collecting and managing data, from on-field survey to sharing projects and ideas. The goal is to learn to manage, in its entirety, the project and the range of possible interventions with deep conservative sensitivity, with skills ranging from maintenance to restoration and redevelopment, both in the dimension of the single building and at the urban and landscape scale.

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“…In Teruggi, Grilli, Russo, Fassi, & Remondino (2020) the method has been improved using a Multi-level Multiresolution approach. The classification process is divided into different stages (Fig.…”

Section: Preliminary Operation: Point Cloud Segmentationmentioning

confidence: 99%

“…6), but the small amount of required labelled data and the speed of the training and classification process make the methodology a success. Performance have been assessed with standard ML metrics (Precision, Recall and F1 score) and results are satisfactory at each classification level (Level 1: 94.7%, 95%, 93%; Level 2: 99%, 98%,99.3%; Level 3: 92%, 88.5%, 91.8% respectively) (Teruggi, Grilli, Russo, Fassi & Remondino, 2020).…”

Section: Preliminary Operation: Point Cloud Segmentationmentioning

confidence: 99%

A Hybrid Model for the Reverse Engineering of the Milan Cathedral. Challenges and Lesson Learnt

Spettu

Teruggi

Canali

et al. 2021

Proceedings ARQUEOLÓGICA 2.0 - 9th International Congress &Amp; 3rd GEORES - GEOmatics and pREServation

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Cultural Heritage (CH) 3D digitisation is getting increasing attention and importance. Advanced survey techniques provide as output a 3D point cloud, wholly and accurately describing even the most complex architectural geometry with a priori established accuracy. These 3D point models are generally used as the base for the realisation of 2D technical drawings and 3D advanced representations. During the last 12 years, the 3DSurveyGroup (3DSG, Politecnico di Milano) conduced an omni-comprehensive, multi-technique survey, obtaining the full point cloud of Milan Cathedral, from which were produced the 2D technical drawings and the 3D model of the Main Spire used by the Veneranda Fabbrica del Duomo di Milano (VF) to plan its periodic maintenance and inspection activities on the Cathedral. Using the survey product directly to plan VF activities would help to skip a long-lasting, uneconomical and manual process of 2D and 3D technical elaboration extraction. In order to do so, the unstructured point cloud data must be enriched with semantics, providing a hierarchical structure that can communicate with a powerful, flexible information system able to effectively manage both point clouds and 3D geometries as hybrid models. For this purpose, the point cloud was segmented using a machine-learning algorithm with multi-level multi-resolution (MLMR) approach in order to obtain a manageable, reliable and repeatable dataset. This reverse engineering process allowed to identify directly on the point cloud the main architectonic elements that are then re-organised in a logical structure inserted inside the informative system built inside the 3DExperience environment, developed by Dassault Systémes.

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A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

Cited by 59 publications

References 50 publications

Connecting Geometry and Semantics via Artificial Intelligence: From 3d Classification of Heritage Data to H-Bim Representations

Connecting Geometry and Semantics via Artificial Intelligence: From 3d Classification of Heritage Data to H-Bim Representations

Enhancing and Managing Data and Digital Competencies for Architecture Teaching and Training in the Field of Protection of Heritage

A Hybrid Model for the Reverse Engineering of the Milan Cathedral. Challenges and Lesson Learnt

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