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 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.

翻译：尽管大规模三维数据集取得了显著进展，但目前交互式三维物体数据集仍然稀缺，且因其构建过程需要大量人工投入而规模受限。我们提出了静态到可开启（S2O）任务，该任务通过可开启部件检测、运动预测与内部几何补全，将静态三维物体转化为可交互的铰接式三维物体。我们构建了一个统一框架来处理此任务，并整理了一个具有挑战性的可开启三维物体数据集，作为系统性评估的测试平台。我们的实验对先前工作的方法以及针对S2O任务的简单而有效的启发式方法进行了基准测试。研究发现，将静态三维物体转化为可交互开启的对应物体是可行的，但所有方法在推广到该任务的实际应用场景时均面临困难，我们进一步指出了未来有前景的研究方向。