Leaping from 2D Detection to Efficient 6DoF Object Pose Estimation

Abstract

Estimating 6DoF object poses from single RGB images is very challenging due to severe occlusions and large search space of camera poses. Keypoint voting based methods have demonstrated its effectiveness and superiority on predicting object poses. However, those approaches are often affected by inaccurate semantic segmentation in computing the keypoint locations. To enable our model to focus on local regions without being distracted by backgrounds, we first localize object regions by a 2D object detector. In doing so, we not only reduce the search space of keypoints but also improve the robustness of the pose estimation. Moreover, since symmetric objects may suffer ambiguity along the symmetric dimension, we propose to select keypoints on the geometrically symmetric locations to resolve the ambiguity. The extensive experimental results on seven different datasets of the BOP challenge benchmark demonstrate that our method outperforms the state-of-the-art and achieves the 3-rd place in the BOP challenge.