SPLATART：结合估计目标结构的关节式高斯溅射
SPLATART: Articulated Gaussian Splatting with Estimated Object Structure
AI总结：
针对机器人领域关节式物体表示难、深度运动学树物体学习难度大的问题，提出SPLATART流水线，解耦部件分割与关节估计任务，可从带位姿图像学习高斯溅射表示，适配更深运动学树结构，在合成与真实数据集上均验证有效。
摘要：
在机器人学领域，关节式物体的表示仍是一个难题。钳子、夹具或橱柜这类物体的表示不仅需要捕捉几何和颜色信息，还需捕捉部件分割、连接关系和关节参数化。此外，每增加一个自由度，学习这些表示的难度都会进一步提升。机械臂等复杂关节式物体可能有7个或更多自由度，其运动学树的深度可能远大于关节式物体研究的典型对象——工具、抽屉和橱柜。为解决这些问题，我们提出SPLATART——一种从带位姿的图像中学习关节式物体高斯溅射表示的流水线，其中部分图像包含图像空间的部件分割标注。SPLATART将部件分割任务与关节估计任务解耦，可事后确定关节估计，还能表示运动学树深度超过以往研究水平的关节式物体。本研究中，我们展示了SPLATART流水线应用于合成Paris数据集物体的相关数据，以及在稀疏分割监督下真实世界物体的定性结果。我们还展示了其在关节式串联链机械臂上的应用，以证明该方法在更深层运动学树结构上的适用性。
英文摘要：
Representing articulated objects remains a difficult problem within the field of robotics. Objects such as pliers, clamps, or cabinets require representations that capture not only geometry and color information, but also part seperation, connectivity, and joint parametrization. Furthermore, learning these representations becomes even more difficult with each additional degree of freedom. Complex articulated objects such as robot arms may have seven or more degrees of freedom, and the depth of their kinematic tree may be notably greater than the tools, drawers, and cabinets that are the typical subjects of articulated object research. To address these concerns, we introduce SPLATART - a pipeline for learning Gaussian splat representations of articulated objects from posed images, of which a subset contains image space part segmentations. SPLATART disentangles the part separation task from the articulation estimation task, allowing for post-facto determination of joint estimation and representation of articulated objects with deeper kinematic trees than previously exhibited. In this work, we present data on the SPLATART pipeline as applied to the syntheic Paris dataset objects, and qualitative results on a real-world object under spare segmentation supervision. We additionally present on articulated serial chain manipulators to demonstrate usage on deeper kinematic tree structures.
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