Leveraging Positional Encoding for Robust Multi-Reference-Based Object
  6D Pose Estimation
Jaewoo Park Jaeguk Kim Nam Ik Cho
Accurately estimating the pose of an object is a crucial task in computer
vision and robotics. There are two main deep learning approaches for this:
geometric representation regression and iterative refinement. However, these
methods have some limitations that reduce their effectiveness. In this paper,
we analyze these limitations and propose new strategies to overcome them. To
tackle the issue of blurry geometric representation, we use positional encoding
with high-frequency components for the object's 3D coordinates. To address the
local minimum problem in refinement methods, we introduce a normalized image
plane-based multi-reference refinement strategy that's independent of intrinsic
matrix constraints. Lastly, we utilize adaptive instance normalization and a
simple occlusion augmentation method to help our model concentrate on the
target object. Our experiments on Linemod, Linemod-Occlusion, and YCB-Video
datasets demonstrate that our approach outperforms existing methods. We will
soon release the code.