Researchers at Osaka University develop a diminished reality system that can display real-time video of a future scene in which a building to be demolished has been digitally removed, which may assist in urban planning and consensus formation
Osaka, Japan – Scientists at Osaka University have created a machine learning system that is capable of virtually removing buildings from a live view. By using generative adversarial networks (GAN) algorithms running on a remote server, the team was able to stream in real-time on a mobile device. This work can help accelerate the process of urban renewal based on community agreement.
Some necessary urban renewal tasks, such as demolishing old buildings, are delayed due to the difficulty in convincing stakeholders to commit resources to a project. For instance, differences in understanding about the plan among building owners and nearby residents may lead to conflict and delays. This may result in a paradox in which tasks would be feasible to begin only after they are already accomplished. Without access to a time machine, this may seem to lead to intractable situations in civil planning.
Now, a team of researchers at Osaka University have help to address this concern in the form of a new algorithm based on machine learning that provides augmented reality real-time video demonstrating the view after a building is removed. “Our method enables users to intuitively understand what the future landscape will look like, which can contribute to reducing the time and cost for forming a consensus,” first author Takuya Kikuchi says. Communication between a mobile device and a server means that all the processing can be done remotely, so any smart phone or tablet can be used at the location of the building. To speed up the algorithm so it can provide real-time augmented video, the team used semantic segmentation on the input image. This allows the deep learning model to classify images pixel by pixel, as opposed to conventional methods that try to perform 3D object detection.
GAN algorithms use two competing neural networks, a generator and a discriminator. The generator is trained to create increasingly realistic images, while the discriminator is tasked with distinguishing if the image was real or artificially generated. “By learning in this way, the GAN algorithm can produce images that do not actually exist but are plausible,” corresponding author Tomohiro Fukuda says. In this case, high accuracy processing was possible as long as the building to be removed from the landscape did not take up more than 15% of the screen. On the basis of field tests, the team was able to achieve virtual demolition video to be streamed at an average rate of 5.71 frames per second, which may greatly assist in on-site community enhancement.
The article was published in Journal of Computational Design and Engineering at DOI: https://doi.org/10.1093/jcde/qwac067.
About Osaka University
Osaka University was founded in 1931 as one of the seven imperial universities of Japan and is now one of Japan's leading comprehensive universities with a broad disciplinary spectrum. This strength is coupled with a singular drive for innovation that extends throughout the scientific process, from fundamental research to the creation of applied technology with positive economic impacts. Its commitment to innovation has been recognized in Japan and around the world, being named Japan's most innovative university in 2015 (Reuters 2015 Top 100) and one of the most innovative institutions in the world in 2017 (Innovative Universities and the Nature Index Innovation 2017). Now, Osaka University is leveraging its role as a Designated National University Corporation selected by the Ministry of Education, Culture, Sports, Science and Technology to contribute to innovation for human welfare, sustainable development of society, and social transformation.