NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarios
En-Te Lin Wei-Jie Lv Ding-Tao Huang Long Zeng
Existing Object Pose Estimation (OPE) methods for stacked scenarios are not
robust to changes in object scale. This paper proposes a new 6DoF OPE network
(NormNet) for different scale objects in stacked scenarios. Specifically, each
object's scale is first learned with point-wise regression. Then, all objects
in the stacked scenario are normalized into the same scale through semantic
segmentation and affine transformation. Finally, they are fed into a shared
pose estimator to recover their 6D poses. In addition, we introduce a new
Sim-to-Real transfer pipeline, combining style transfer and domain
randomization. This improves the NormNet's performance on real data even if we
only train it on synthetic data. Extensive experiments demonstrate that the
proposed method achieves state-of-the-art performance on public benchmarks and
the MultiScale dataset we constructed. The real-world experiments show that our
method can robustly estimate the 6D pose of objects at different scales.