Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot,
  Generalizable Approach using RGB Images
Panwang Pan Zhiwen Fan Brandon Y. Feng Peihao Wang Chenxin Li Zhangyang Wang
The accurate estimation of six degrees-of-freedom (6DoF) object poses is
essential for many applications in robotics and augmented reality. However,
existing methods for 6DoF pose estimation often depend on CAD templates or
dense support views, restricting their usefulness in realworld situations. In
this study, we present a new cascade framework named Cas6D for few-shot 6DoF
pose estimation that is generalizable and uses only RGB images. To address the
false positives of target object detection in the extreme few-shot setting, our
framework utilizes a selfsupervised pre-trained ViT to learn robust feature
representations. Then, we initialize the nearest top-K pose candidates based on
similarity score and refine the initial poses using feature pyramids to
formulate and update the cascade warped feature volume, which encodes context
at increasingly finer scales. By discretizing the pose search range using
multiple pose bins and progressively narrowing the pose search range in each
stage using predictions from the previous stage, Cas6D can overcome the large
gap between pose candidates and ground truth poses, which is a common failure
mode in sparse-view scenarios. Experimental results on the LINEMOD and GenMOP
datasets demonstrate that Cas6D outperforms state-of-the-art methods by 9.2%
and 3.8% accuracy (Proj-5) under the 32-shot setting compared to OnePose++ and
Gen6D.