@inproceedings{811345128a514f538a06a6738621f7bf,
title = "Object Detection in Floor Plans for Automated VR Environment Generation",
abstract = "The development of visually compelling Virtual Reality (VR) environments for serious games is a complex task. Most environments are designed using game engines such as Unity or Unreal Engine and require hours if not days of work. However, most important information of indoor environments can be represented by floor plans. Those have been used in architecture for centuries as a fast and reliable way of depicting building configurations. Therefore, the idea of easing the creation of VR ready environments using floor plans is of great interest. In this paper we propose an automated framework to detect and classify objects in floor plans using a neural network trained with a custom floor plan dataset generator.",
keywords = "Floor Plans, Image Recognition, Neural Networks, Synthetic Data",
author = "Timoth{\'e}e Fr{\'e}ville and Charles Hamesse and B{\^e}noit Pairet and Rob Haelterman",
note = "Publisher Copyright: {\textcopyright} 2022 by SCITEPRESS-Science and Technology Publications, Lda.; 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2023 ; Conference date: 19-02-2023 Through 21-02-2023",
year = "2023",
doi = "10.5220/0011629300003417",
language = "English",
volume = "5",
series = "Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications",
publisher = "SciTePress",
pages = "480--486",
booktitle = "Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP",
}