SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded
  Objects
Ning Gao Ngo Anh Vien Hanna Ziesche Gerhard Neumann
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.