NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarios
This addresses robustness in object pose estimation for robotics or AR applications, but it is incremental as it builds on existing methods with a scale normalization approach.
The paper tackles the problem of 6D pose estimation for objects of varying scales in stacked scenarios, proposing NormNet which normalizes object scales and uses a shared pose estimator, achieving state-of-the-art performance on public benchmarks and a new MultiScale dataset.
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.