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- Title:
Layout Prediction in Real-world Construction Site Images
- Document type:
- Forschungsdaten
- Publication date:
- 22.08.2024
- Responsible:
- Vega-Torres, Miguel A.
- Authors:
- Vega-Torres, Miguel A.; Suci, Hünkar; Hsu, Ling-Hsuan; Kuhada, Sonali; Mangukiya,Sahil; Borrmann, André
- Author affiliation:
- TUM
- Publisher:
- TUM
- Identifier:
- doi:10.14459/2024mp1751462
- End date of data production:
- 01.01.2024
- Subject area:
- BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; TEC Technik, Ingenieurwissenschaften (allgemein)
- Resource type:
- Abbildungen von Objekten / image of objects
- Data type:
- Bilder / images ; Texte / texts
- Other data type:
- - Annotations in JSON format.
- Code used to generate the JSON file in python. - Description:
- The Layout Prediction dataset contains labels for 229 images of real-world construction sites, which are part of the Sequence 2 of the ConSLAM dataset ( https://github.com/mac137/ConSLAM ). This dataset adheres to the conventions defined in SRW-Net ( https://github.com/DavidGillsjo/SRW-Net ).
The dataset includes the original images accompanied by layout annotations. These annotations consist of lines representing various architectural elements, such as walls, ceilings, and doors, with each line specified by coordinates and categorized in a dictionary according to its type. Junctions are also annotated and classified as either "proper" or "false." A "proper" junction indicates an actual endpoint of a line in the real world, while a "false" junction appears to end in the image but does not correspond to a physical endpoint.
The annotated elements are color-coded and stored in a JSON file. The resulting layouts are illustrated in figures, and the Python code used to generate the annotation files is also provided. Please refer to the "Legend.png" file in the dataset for the corresponding color codes of the lables. - Method of data assessment:
- The dataset was generated using python and manual pixel selection process.
- Key words:
- Cameara; Localization; Room Layout; BIM; Construction Site
- Technical remarks:
- View and download (1,08 GB total, 915 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1751462):
rsync rsync://m1751462@dataserv.ub.tum.de/m1751462/ - Language:
- en
- Rights:
- by-nc, http://creativecommons.org/licenses/by-nc/4.0
- Horizon 2020:
- INTREPID Horizon 2020 Grant agreement ID: 883345
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