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Title:

Synthetic Brick Dataset for Object Detection and 6-DoF Pose Estimation

Document type:
Forschungsdaten
Publication date:
08.07.2026
Responsible:
Saral, Begüm
Authors:
Saral, Begüm; Chen, Hanzhi; Leutenegger, Stefan; Dörfler, Kathrin
Author affiliation:
TUM
Publisher:
TUM
Identifier:
doi:10.14459/2026mp1858028
End date of data production:
17.10.2025
Subject area:
ARC Architektur; BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik
Resource type:
Simulationen / simulations
Data type:
Bilder / images ; Datenbanken / data bases
Description:
A photorealistic synthetic brick dataset generated for training deep learning models for object detection and 6-DoF pose estimation. The dataset comprises RGB images with segmentation masks and COCO annotations, capturing individual bricks embedded in mortar-occluded brickwork structures. To improve model robustness and generalization, the scenes incorporate randomized brickwork geometries, varying numbers of bricks, and diverse environmental conditions.
Method of data assessment:
The synthetic data generator leverages open-source tools, including Blender-based Python libraries such as bpy, geometry_script, and blenderproc, to create large datasets of rendered images with randomised parameters. In this work, the generator is configured to model the specific brick type used in the real-world experiments, with physical dimensions of 240 × 115 × 50 mm and a light reddish surface texture, alongside synthetic mortar using randomised noise textures, with controlled variation in...     »
Links:

https://github.com/augmentedfabricationlab/synthetic_brick_data_generation
Corresponding Paper: https://www.doi.org/10.1007/s41693-026-00185-1

Key words:
Synthetic photorealistic data generation; Deep learning-based object perception; Brick dataset; Object detection; 6-DoF pose estimation
Technical remarks:
View ( 16 GB total, 93449 Files)
The data server offers downloads with FTP
The data server offers downloads with rsync (password m1858028):
rsync rsync://m1858028@dataserv.ub.tum.de/m1858028/
Language:
en
Rights:
by, http://creativecommons.org/licenses/by/4.0
Funding Information:
TUM Georg Nemetschek Institute Artificial Intelligence for the Built World - SPAICR
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