DCNet: Dense Correspondence Neural Network for 6DoF Object Pose Estimation in Occluded Scenes

6DoF object pose estimation is essential for many real-world applications. Although great progress has been made, challenges still remain in estimating 6D pose for occluded objects. Current RGB-D approaches predict 6DoF pose directly, which is sensitive to occlusion in cluttered scenes. In this work, we propose DCNet, an end-to-end framework for estimating 6DoF object poses. DCNet first converts pixels in the image plane to point clouds in the camera coordinate system and then establishes dense correspondences between the camera coordinate system and the object coordinate system. Based on these two systems, we fuse 2D appearance and 3D geometric features by pixel-wise concatenation to construct dense correspondences, from which the pose is calculated through the least-squares fitting algorithm. Dense correspondences guarantee enough point pairs for a robust 6DoF pose estimation, even if the occlusion is heavy. Experimental results demonstrate that DCNet outperforms the state-of-the-art methods on LINEMOD, Occlusion LINEMOD and YCB-Video datasets, especially in terms of the robustness to occlusion scenes.