将静态3D物体转为可交互开合物体，提升机器人操作与虚拟智能体的可用性。
S2O: Static to Openable Enhancement for Articulated 3D Objects
- 通过检测可开合部件、预测运动轨迹、补全内部结构实现静态到可交互转换。
- 构建了首个大规模可开合3D物体数据集，支持系统性评估该任务。
- 方法在真实场景下泛化能力弱，但为后续研究指明方向，适合机器人与AI应用者参考。
尽管大型3D数据集已取得显著进展，但目前仍缺乏交互式3D物体数据集，且规模受限于人工构建成本。本文提出静态到可开合（S2O）任务，通过可开合部件检测、运动预测和内部几何补全，将静态3D物体转化为可交互的关节物体。我们提出统一框架，并构建了一个具有挑战性的可开合3D物体数据集，作为系统评估的基准。实验对先前方法、改进方法及简单有效启发式策略进行了基准测试。结果表明，将静态3D物体转化为可交互开合版本是可行的，但所有方法在真实任务设置中均难以泛化，本文也指出了未来有前景的研究方向。本工作实现了交互式3D物体的高效生成，适用于机器人操作与具身智能任务。
原文摘要 · Abstract (English)
Despite much progress in large 3D datasets there are currently few interactive 3D object datasets, and their scale is limited due to the manual effort required in their construction. We introduce the static to openable (S2O) task which creates interactive articulated 3D objects from static counterparts through openable part detection, motion prediction, and interior geometry completion. We formulate a unified framework to tackle this task, and curate a challenging dataset of openable 3D objects that serves as a test bed for systematic evaluation. Our experiments benchmark methods from prior work, extended and improved methods, and simple yet effective heuristics for the S2O task. We find that turning static 3D objects into interactively openable counterparts is possible but that all methods struggle to generalize to realistic settings of the task, and we highlight promising future work directions. Our work enables efficient creation of interactive 3D objects for robotic manipulation and embodied AI tasks.
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