DON6D: Decoupled One-stage Network for 6D Pose Estimation

Abstract

6D pose object estimation is a key task in robotic manipulation and grasping scenes. Many prior two-stage works with slower inference speeds require extra refinement to deal with challenge problems such as variations of lighting, sensor noise, object occlusion, and truncation. To address these problems, this work proposes a decoupled one-stage network (DON6D) for 6D pose estimation, which improves inference speed, on the premise of maintaining accuracy. Specifically, since RGB images are aligned with RGB-D images, DON6D first utilizes a 2D detection network to locate the interested objects in RGB-D images. Second, DON6D includes a module of feature extraction and fusion to fully extract color and geometric features. Furthermore, dual data augmentation is implemented to enhance the generalizability of the model. Third, DON6D fuses them and applies an attention residual encoder-decoder which can boost pose estimation performance to obtain accurate 6D pose. DON6D is a real-time method that is evaluated on the LINEMOD dataset and YCB-Video dataset. The results demonstrate that DON6D is superior to several state-of-the-art methods under the ADD(-S) metric and ADD(-S) AUC metric.