SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D
  Object Pose Estimation
Fabian Duffhauss Sebastian Koch Hanna Ziesche Ngo Anh Vien Gerhard Neumann
Detecting objects and estimating their 6D poses is essential for automated
systems to interact safely with the environment. Most 6D pose estimators,
however, rely on a single camera frame and suffer from occlusions and
ambiguities due to object symmetries. We overcome this issue by presenting a
novel symmetry-aware multi-view 6D pose estimator called SyMFM6D. Our approach
efficiently fuses the RGB-D frames from multiple perspectives in a deep
multi-directional fusion network and predicts predefined keypoints for all
objects in the scene simultaneously. Based on the keypoints and an instance
semantic segmentation, we efficiently compute the 6D poses by least-squares
fitting. To address the ambiguity issues for symmetric objects, we propose a
novel training procedure for symmetry-aware keypoint detection including a new
objective function. Our SyMFM6D network significantly outperforms the
state-of-the-art in both single-view and multi-view 6D pose estimation. We
furthermore show the effectiveness of our symmetry-aware training procedure and
demonstrate that our approach is robust towards inaccurate camera calibration
and dynamic camera setups.