ArtiWorld：LLM驱动的场景中3D物体的关节化
ArtiWorld: LLM-Driven Articulation of 3D Objects in Scenes
AI总结：
针对模拟中刚性3D资产多、手动转换关节式资产成本高的问题，提出ArtiWorld场景感知管道，以Arti4URDF为核心，利用3D点云、LLM先验及URDF提示设计，自动生成保留几何形状的可执行URDF关节模型，在三类场景评估中性能领先，为构建机器人模拟环境提供实用路径。
摘要：
构建交互式模拟器和可扩展的机器人学习环境需要大量关节式资产。然而，模拟中大多数现有3D资产是刚性的，手动将其转换为关节式物体极为耗费人力和成本。这引发了一个自然问题：我们能否自动识别场景中的可关节化物体并直接将其转换为关节式资产？本文提出ArtiWorld，这是一个场景感知管道，可从文本场景描述中定位候选可关节化物体，并重建保留原始几何形状的可执行URDF模型。该管道的核心是Arti4URDF，它利用3D点云、大型语言模型（LLM）的先验知识以及面向URDF的提示设计，在保持3D形状的同时快速将刚性物体转换为基于URDF的交互式关节式物体。我们在三个层面评估ArtiWorld：3D模拟物体、完整3D模拟场景和真实世界扫描场景。在所有三种设置中，我们的方法持续优于现有方法并达到最先进性能，同时保留物体几何形状并正确捕捉物体交互性以生成可用的基于URDF的关节式模型。这为直接从现有3D资产构建交互式、机器人就绪的模拟环境提供了实用路径。代码和数据将发布。
英文摘要：
Building interactive simulators and scalable robot-learning environments requires a large number of articulated assets. However, most existing 3D assets in simulation are rigid, and manually converting them into articulated objects is extremely labor- and cost-intensive. This raises a natural question: can we automatically identify articulable objects in a scene and convert them into articulated assets directly? In this paper, we present ArtiWorld, a scene-aware pipeline that localizes candidate articulable objects from textual scene descriptions and reconstructs executable URDF models that preserve the original geometry. At the core of this pipeline is Arti4URDF, which leverages 3D point cloud, prior knowledge of a large language model (LLM), and a URDF-oriented prompt design to rapidly convert rigid objects into interactive URDF-based articulated objects while maintaining their 3D shape. We evaluate ArtiWorld at three levels: 3D simulated objects, full 3D simulated scenes, and real-world scan scenes. Across all three settings, our method consistently outperforms existing approaches and achieves state-of-the-art performance, while preserving object geometry and correctly capturing object interactivity to produce usable URDF-based articulated models. This provides a practical path toward building interactive, robot-ready simulation environments directly from existing 3D assets. Code and data will be released.
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。
