2017
DOI: 10.5194/isprs-annals-iv-2-w2-203-2017
|View full text |Cite
|
Sign up to set email alerts
|

Point Cloud Classification of Tesserae From Terrestrial Laser Data Combined With Dense Image Matching for Archaeological Information Extraction

Abstract: ABSTRACT:Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, eac… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
15
0

Year Published

2017
2017
2024
2024

Publication Types

Select...
8
2

Relationship

0
10

Authors

Journals

citations
Cited by 20 publications
(15 citation statements)
references
References 29 publications
0
15
0
Order By: Relevance
“…• semantic annotation of the models: it can be useful to deepen the analysis and interpretation of the architecture as much as producing an aware representation of the models (Poux et al, 2017;Grilli et al, 2018); • automatic recognition of similar architectural elements in vast datasets: the detection of similar geometric properties in the scene can help the identification of a prevalent architectural style or constructive technique and could be a requisite for HBIM applications; • identification and distinction of structural and decorative architectural elements, highlighting their spatial distribution and organization. But the classification of heritage 3D data is more challenging for various reasons:…”
Section: Introductionmentioning
confidence: 99%
“…• semantic annotation of the models: it can be useful to deepen the analysis and interpretation of the architecture as much as producing an aware representation of the models (Poux et al, 2017;Grilli et al, 2018); • automatic recognition of similar architectural elements in vast datasets: the detection of similar geometric properties in the scene can help the identification of a prevalent architectural style or constructive technique and could be a requisite for HBIM applications; • identification and distinction of structural and decorative architectural elements, highlighting their spatial distribution and organization. But the classification of heritage 3D data is more challenging for various reasons:…”
Section: Introductionmentioning
confidence: 99%
“…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%
“…Other approaches may also use machine learning in the image space (Grilli et al, 2018) or 3D object space (Bassier et al, 2017b). After the classification step is completed, semantic information still needs to be annotated to each segmented part in order to create a semantically rich point cloud (Poux et al, 2017).…”
Section: Related Workmentioning
confidence: 99%