Shape-Constraint Recurrent Flow for 6D Object Pose Estimation
Yang Hai Rui Song Jiaojiao Li Yinlin Hu
Most recent 6D object pose methods use 2D optical flow to refine their
results. However, the general optical flow methods typically do not consider
the target's 3D shape information during matching, making them less effective
in 6D object pose estimation. In this work, we propose a shape-constraint
recurrent matching framework for 6D object pose estimation. We first compute a
pose-induced flow based on the displacement of 2D reprojection between the
initial pose and the currently estimated pose, which embeds the target's 3D
shape implicitly. Then we use this pose-induced flow to construct the
correlation map for the following matching iterations, which reduces the
matching space significantly and is much easier to learn. Furthermore, we use
networks to learn the object pose based on the current estimated flow, which
facilitates the computation of the pose-induced flow for the next iteration and
yields an end-to-end system for object pose. Finally, we optimize the optical
flow and object pose simultaneously in a recurrent manner. We evaluate our
method on three challenging 6D object pose datasets and show that it
outperforms the state of the art significantly in both accuracy and efficiency.