| id |
ecaade2017_041 |
| authors |
Fukuda, Tomohiro, Kuwamuro, Yasuyuki and Yabuki, Nobuyoshi |
| year |
2017 |
| title |
Optical Integrity of Diminished Reality Using Deep Learning |
| doi |
https://doi.org/10.52842/conf.ecaade.2017.1.241
|
| source |
Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 241-250 |
| summary |
A new method is proposed to improve diminished reality (DR) simulations to allow the demolition and removal of entire buildings in large-scale spaces. Our research goal was to obtain optical integrity by using a scientific and reliable simulation approach. Further, we tackled presumption of the texture of the background sky by applying deep learning. Our approach extracted the background sky using information from the actual sky obtained from a photographed image. This method comprised two steps: (1) detection of the sky area from the image through image segmentation and (2) creation of an image of the sky through image inpainting. The deep convolutional neural networks developed by us to train and predict images were evaluated to be feasible and effective. |
| keywords |
Diminished Reality; Optical Integrity; Deep Learning; Augmented Reality; Landscape assessment |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (16,224,315 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:50 |
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