6D Object Pose Estimation with Attention Aware Bi-gated Fusion

Laichao Wang, Weiding Lu, Yuan Tian, Yong Guan, Zhenzhou Shao, Zhi-Ping Shi

Abstract: Accurate object pose estimation is a prerequisite for successful robotic grasping tasks. Currently keypoint-based pose estimation methods using RGB-D data have shown promising results in simple environments. However, how to fuse the complementary features from RGB-D data is still a challenging task. To this end, this paper proposes a two-branch network with attention aware bi-gated fusion (A2BF) module for the keypoint-based 6D object pose estimation, named A2BNet for abbreviation. A2BF module consists of two key components, bidirectional gated fusion and attention mechanism modules to effectively extract information from both RGB and point cloud data, prioritizing crucial details while disregarding irrelevant information. Several A2BF modules can be embedded in the network to generate complementary texture and geometric information. Extensive experiments are conducted on the public LineMOD and Occlusion LineMOD datasets. Experimental results demonstrate that the average accuracy using the proposed method on both datasets can reach 99.8% and 67.6% respectively, outperforms the state-of-the-art methods.

External IDs:dblp:conf/iconip/WangLTGSS23