FurniScene：一个包含精细家具陈设场景的大规模3D房间数据集
FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes
AI总结：
针对现有室内场景生成数据集缺乏小型陈设品、导致生成结果多样性和真实感不足的问题，本文提出包含11,698个房间和39,691个家具CAD模型的大规模精细3D房间数据集FurniScene，并设计两阶段扩散场景模型TSDSM及评估基准，显著提升室内场景生成的真实感。
摘要：
室内场景生成近年来引起了广泛关注，因为它在游戏、虚拟现实和室内设计等应用中至关重要。当前的室内场景生成方法虽然能够生成合理的房间布局，但往往缺乏多样性和真实感。这主要是由于现有数据集的覆盖范围有限，仅包含大型家具，而缺少日常生活中的小型陈设品。为了解决这些挑战，我们提出了FurniScene，一个由室内设计专业人士制作的大规模3D房间数据集，包含精细的家具陈设场景。具体而言，FurniScene包含11,698个房间和39,691个独特的家具CAD模型，涵盖89种不同类型，从大床到咖啡桌上的小茶杯均有覆盖。为了更好地适应细粒度室内场景布局生成，我们引入了一种新颖的两阶段扩散场景模型（TSDSM），并基于FurniScene构建了面向多种室内场景生成方法的评估基准。定量和定性评估证明了我们的方法能够生成高度逼真的室内场景。我们的数据集和代码将很快公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
