FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing
  Scenes
Genghao Zhang Yuxi Wang Chuanchen Luo Shibiao Xu Junran Peng Zhaoxiang Zhang Man Zhang
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 interior
design professionals. Specifically, the FurniScene consists of 11,698 rooms and
39,691 unique furniture CAD models with 89 different types, covering things
from large beds to small teacups on the coffee table. To better suit
fine-grained indoor scene layout generation, we introduce a novel Two-Stage
Diffusion Scene Model (TSDSM) and conduct an evaluation benchmark for various
indoor scene generation based on FurniScene. Quantitative and qualitative
evaluations demonstrate the capability of our method to generate highly
realistic indoor scenes. Our dataset and code will be publicly available soon.