Diorama：释放零样本单视图3D室内场景建模能力
Diorama: Unleashing Zero-shot Single-view 3D Indoor Scene Modeling
AI总结：
本文提出Diorama零样本开放世界系统，通过分解建筑重建、形状检索、位姿估计与布局优化等子任务，从单视图RGB图像构建可交互3D室内场景。
摘要：
利用CAD对象从RGB图像重建结构化3D场景，可带来高效且紧凑的场景表示，并保持组合性与可交互性。现有工作提出了依赖大量训练的方法，这些方法要么依赖昂贵却不准确的真实世界标注，要么依赖可控但单调的合成数据，无法很好地泛化到未见过的对象或领域。我们提出Diorama，这是首个无需端到端训练或人工标注，即可从单视图RGB观测中对3D场景进行整体建模的零样本开放世界系统。我们通过将问题分解为多个子任务，并为每个子任务引入鲁棒、可泛化的解决方案，证明了该方法的可行性：建筑结构重建、3D形状检索、对象位姿估计和场景布局优化。我们在合成数据与真实世界数据上评估了该系统，结果表明其显著优于以往工作的基线。我们还展示了其对互联网图像和文本到场景任务的泛化能力。
英文摘要：
Reconstructing structured 3D scenes from RGB images using CAD objects unlocks efficient and compact scene representations that maintain compositionality and interactability. Existing works propose training-heavy methods relying on either expensive yet inaccurate real-world annotations or controllable yet monotonous synthetic data that do not generalize well to unseen objects or domains. We present Diorama, the first zero-shot open-world system that holistically models 3D scenes from single-view RGB observations without requiring end-to-end training or human annotations. We show the feasibility of our approach by decomposing the problem into subtasks and introduce robust, generalizable solutions to each: architecture reconstruction, 3D shape retrieval, object pose estimation, and scene layout optimization. We evaluate our system on both synthetic and real-world data to show we significantly outperform baselines from prior work. We also demonstrate generalization to internet images and the text-to-scene task.
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。
