Osaka, Japan – Scientists from the Division of Sustainable Energy and Environmental Engineering at Osaka University used generative adversarial networks trained on a custom dataset to virtually remove obstructions from building façade images. This work may assist in civic planning as well as computer vision applications.
The ability to digitally “erase” unwanted occluding objects from a cityscape is highly useful but requires a great deal of computing power. Previous methods used standard image datasets to train machine learning algorithms. Now, a team of researchers at Osaka University have built a custom dataset as part of a general framework for the automatic removal of unwanted objects — such as pedestrians, riders, vegetation, or cars — from an image of a building’s façade. The removed region was replaced using digital inpainting to efficiently restore a complete view.
The researchers used data from the Kansai region of Japan in an open-source street view service, as opposed to the conventional building image sets often used in machine learning for urban landscapes. Then they constructed a dataset to train an adversarial generative network (GAN) for inpainting the occluded regions with high accuracy. “For the task of façade inpainting in street-level scenes, we adopted an end-to-end deep learning-based image inpainting model by training with our customized datasets,” first author Jiaxin Zhang explains.
The team used semantic segmentation to detect several types of obstructing objects, including pedestrians, vegetation, and cars, as well as using GANs for filling the detected regions with background textures and patching information from street-level imagery. They also proposed a workflow to automatically filter unblocked building façades from street view images and customized the dataset to contain both original and masked images to train additional machine learning algorithms.
This visualization technology offers a communication tool for experts and non-experts, which can help develop a consensus on future urban environmental designs. “Our system was shown to be more efficient compared with previously employed methods when dealing with urban landscape projects for which background information was not available in advance,” senior author Tomohiro Fukuda explains. In the future, this approach may be used to help design augmented reality systems that can automatically remove existing buildings and instead show proposed renovations.
The article, “Automatic object removal with obstructed façades completion using semantic segmentation and generative adversarial inpainting” was published in IEEE Access at DOI: https://doi.org/10.1109/ACCESS.2021.3106124
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.
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Automatic object removal with obstructed façades completion using semantic segmentation and generative adversarial inpainting
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