Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation
In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms b...
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| Vydané v: | ISPRS international journal of geo-information Ročník 9; číslo 9; s. 535 |
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| Jazyk: | English |
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01.09.2020
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| Abstract | In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented. |
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| AbstractList | In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented. In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented. Keywords: classification; semantic segmentation; digital cultural heritage; point clouds; machine learning; deep learning |
| Audience | Academic |
| Author | Grilli, Eleonora Pierdicca, Roberto Matrone, Francesca Remondino, Fabio Paolanti, Marina Martini, Massimo |
| Author_xml | – sequence: 1 givenname: Francesca orcidid: 0000-0002-9160-1674 surname: Matrone fullname: Matrone, Francesca – sequence: 2 givenname: Eleonora orcidid: 0000-0003-3400-9364 surname: Grilli fullname: Grilli, Eleonora – sequence: 3 givenname: Massimo orcidid: 0000-0003-1714-4310 surname: Martini fullname: Martini, Massimo – sequence: 4 givenname: Marina orcidid: 0000-0002-5523-7174 surname: Paolanti fullname: Paolanti, Marina – sequence: 5 givenname: Roberto orcidid: 0000-0002-9160-834X surname: Pierdicca fullname: Pierdicca, Roberto – sequence: 6 givenname: Fabio orcidid: 0000-0001-6097-5342 surname: Remondino fullname: Remondino, Fabio |
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| SubjectTerms | Algorithms Annotations Architectural elements Architecture Artificial intelligence Case studies Classification computer vision Cultural heritage Cultural resources data collection Datasets Deep learning digital cultural heritage digital images Historic sites image analysis Image segmentation Lasers Learning algorithms lidar Machine learning Masonry Methods Photogrammetry point clouds Semantic segmentation Semantics Three dimensional models |
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| Title | Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation |
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