To enable meaningful robotic manipulation of objects in the real-world, 6D
pose estimation is one of the critical aspects. Most existing approaches have
difficulties to extend predictions to scenarios where novel object instances
are continuously introduced, especially with heavy occlusions. In this work, we
propose a few-shot pose estimation (FSPE) approach called SA6D, which uses a
self-adaptive segmentation module to identify the novel target object and
construct a point cloud model of the target object using only a small number of
cluttered reference images. Unlike existing methods, SA6D does not require
object-centric reference images or any additional object information, making it
a more generalizable and scalable solution across categories. We evaluate SA6D
on real-world tabletop object datasets and demonstrate that SA6D outperforms
existing FSPE methods, particularly in cluttered scenes with occlusions, while
requiring fewer reference images.

通过使用自适应分割模块并仅使用少量混乱的参考图像，我们提出了一种少样本姿态估计（FSPE）方法 SA6D，它能够对新的目标对象进行准确预测并构建目标对象的点云模型。与现有方法不同，SA6D 不需要以物体为中心的参考图像或任何其他物体信息，使其成为一个更具通用性和可伸缩性的解决方案，并在真实世界的台面物体数据集上进行了评估，证明了在带有遮挡的混乱环境中，SA6D 优于现有的 FSPE 方法，并且所需的参考图像较少。