Segmentation of 3d Models for Cultural Heritage Structural
Analysis – Some Critical Issues

Barsanti, Sara Gonizzi; Guidi, Gabriele; Luca, Livio de

doi:10.5194/isprs-annals-iv-2-w2-115-2017

Cited by 25 publications

(15 citation statements)

References 16 publications

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“…Despite some works that attempt at classifying DCH images by employing different kinds of techniques [33][34][35][36] already exist, there are still few researches who seek to directly exploit the Point Clouds of CH for semantic classification or segmentation through ML [37] or DL techniques. One of them is [38], where a segmentation of 3D models of historical buildings is proposed for FEA analysis, starting from Point Clouds and meshes. Barsanti et al tested some algorithms such as the region growing, directly on the Point Clouds, proving its effectiveness for the segmentation of flat and well-defined structures, nevertheless more complex geometries such as curves or gaps have not been correctly segmented and the computational times increased considerably.…”

Section: Classification and Semantic Segmentation In The Field Of Dchmentioning

confidence: 99%

Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

et al. 2020

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In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.

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Section: Classification and Semantic Segmentation In The Field Of Dchmentioning

confidence: 99%

Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

et al. 2020

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“…Many experiments were also carried out on 3D data at different scales [6,81,82]. Some works aim to define a procedure for the integration of architectural 3D models within BIM [1,5,83].…”

Section: Segmentation and Classification In Cultural Heritagementioning

confidence: 99%

“…Recent years have witnessed significant progress in automatic procedures for segmentation and classification of point clouds or meshes [2][3][4]. There are multiple studies related to the segmentation topic, mainly driven by specific needs provided by the field of application (Building Information Modeling (BIM) [5], heritage documentation and preservation [6], robotics [7] autonomous driving [8], urban planning [9], etc.) In the cultural heritage field, the identification of different components ( Figure 1) in point clouds and 3D meshes is of primary importance because it can facilitate the study of monuments and integrating them with heterogeneous information and attributes.…”

Section: Introductionmentioning

confidence: 99%

Classification of 3D Digital Heritage

2019

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In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better interpret the object under study. Indeed, the high complexity of 3D data along with the large diversity of heritage assets themselves have constituted segmentation and classification methods as currently active research topics. Although machine learning methods brought great progress in this respect, few advances have been developed in relation to cultural heritage 3D data. Starting from the existing literature, this paper aims to develop, explore and validate reliable and efficient automated procedures for the classification of 3D data (point clouds or polygonal mesh models) of heritage scenarios. In more detail, the proposed solution works on 2D data (“texture-based” approach) or directly on the 3D data (“geometry-based approach) with supervised or unsupervised machine learning strategies. The method was applied and validated on four different archaeological/architectural scenarios. Experimental results demonstrate that the proposed approach is reliable and replicable and it is effective for restoration and documentation purposes, providing metric information e.g. of damaged areas to be restored.

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“…Barsanti et al [35] evaluated a region-growing algorithm to group points into clusters based on the angular comparison between locally estimated surface normals. They concluded that working on PCs does not seem to be the most suitable approach for creating a 3D segmentation to analyze historic concrete structures.…”

Section: Limitations Of Previous Workmentioning

confidence: 99%

Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition

Hadavandsiri

Lichti

Jahraus

et al. 2019

IJGI

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This paper presents a novel approach for automatic, preliminary detection of damage in concrete structures using ground-based terrestrial laser scanners. The method is based on computation of defect-sensitive features such as the surface curvature, since the surface roughness changes strongly if an area is affected by damage. A robust version of principal component analysis (PCA) classification is proposed to distinguish between structural damage and outliers present in the laser scanning data. Numerical simulations were conducted to develop a systematic point-wise defect classifier that automatically diagnoses the location of superficial damage on the investigated region. The method provides a complete picture of the surface health of concrete structures. It has been tested on two real datasets: a concrete heritage aqueduct in Brooks, Alberta, Canada; and a civil pedestrian concrete structure. The experiment results demonstrate the validity and accuracy of the proposed systematic framework for detecting and localizing areas of damage as small as 1 cm or less.

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Segmentation of 3d Models for Cultural Heritage Structural Analysis – Some Critical Issues

Cited by 25 publications

References 16 publications

Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

Classification of 3D Digital Heritage

Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition

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