FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916235939151872 |
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| author | Zhang, Genghao Wang, Yuxi Luo, Chuanchen Xu, Shibiao Zhang, Zhaoxiang Zhang, Man Peng, Junran |
| author_facet | Zhang, Genghao Wang, Yuxi Luo, Chuanchen Xu, Shibiao Zhang, Zhaoxiang Zhang, Man Peng, Junran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes Zhang, Genghao Wang, Yuxi Luo, Chuanchen Xu, Shibiao Zhang, Zhaoxiang Zhang, Man Peng, Junran Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.03470 |