MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis

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dc.contributor.authorRen, Haochengen_US
dc.contributor.authorZhang, Haoen_US
dc.contributor.authorZheng, Jiaen_US
dc.contributor.authorZheng, Jiaxiangen_US
dc.contributor.authorTang, Ruien_US
dc.contributor.authorHuo, Yuchien_US
dc.contributor.authorBao, Hujunen_US
dc.contributor.authorWang, Ruien_US
dc.contributor.editorUmetani, Nobuyukien_US
dc.contributor.editorWojtan, Chrisen_US
dc.contributor.editorVouga, Etienneen_US
dc.date.accessioned2022-10-04T06:39:31Z
dc.date.available2022-10-04T06:39:31Z
dc.date.issued2022
dc.description.abstractWith the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain-Specific Language, allowing users to select scenes from the commercial indoor scene database, synthesize scenes for different tasks with customized rules, and render various types of imagery data, such as color images, geometric structures, semantic labels. Our system eases the difficulty of customizing massive scenes for different tasks and relieves users from manipulating fine-grained scene configurations by providing user-controllable randomness using multilevel samplers. Most importantly, it empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets, e.g., 3D CAD models. We demonstrate the validity and flexibility of our system by using our synthesized data to improve the performance on different kinds of computer vision tasks. The project page is at https://coohom.github.io/MINERVAS.en_US
dc.description.number7
dc.description.sectionheadersPoint Cloud Processing and Dataset Generation
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14657
dc.identifier.issn1467-8659
dc.identifier.pages63-74
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14657
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14657
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Graphics systems and interfaces
dc.subjectComputing methodologies → Graphics systems and interfaces
dc.titleMINERVAS: Massive INterior EnviRonments VirtuAl Synthesisen_US
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