May 7, 2024, 4:48 a.m. | Genghao Zhang, Yuxi Wang, Chuanchen Luo, Shibiao Xu, Zhaoxiang Zhang, Man Zhang, Junran Peng

cs.CV updates on arXiv.org arxiv.org

arXiv:2401.03470v2 Announce Type: replace
Abstract: Indoor scene generation has attracted significant attention recently as it is crucial for applications of gaming, virtual reality, and interior design. Current indoor scene generation methods can produce reasonable room layouts but often lack diversity and realism. This is primarily due to the limited coverage of existing datasets, including only large furniture without tiny furnishings in daily life. To address these challenges, we propose FurniScene, a large-scale 3D room dataset with intricate furnishing scenes from …

abstract applications arxiv attention coverage cs.ai cs.cv current dataset design diversity gaming interior design reality room scale type virtual virtual reality

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