Most category-level object pose estimation methods are multi-tasking, including instance segmentation, Normalized Object Coordinate Space (NOCS) map estimation and classification. However, previous approaches overlooked the connection between multiple tasks. In this work, we propose an efficient network to make better use of the complementarity between different tasks. Specifically, we propose an external sharing unit (ESU) to promote instance segmentation and NOCS map estimation. In addition, we propose an internal sharing unit (ISU) to improve the NOCS map estimation. The NOCS map head has three branches. And the estimated coordinates of each branch have strong correlation. Extensive experiments on the CAMERA and REAL dataset demonstrate the effectiveness of joint optimization in multi-tasking category-level object estimation. Experimental results also show that the proposed method can improve not only accuracy but also efficiency on several benchmarks.

大多数类别级别的对象姿态估计方法都是多任务处理，包括实例分割，归一化对象坐标空间（NOCS）映射估计和分类。但是，以前的方法忽略了多个任务之间的联系。在这项工作中，我们提出了一个有效的网络，可以更好地利用不同任务之间的互补性。具体来说，我们提出了一个外部共享单元（ESU）来促进实例分割和NOCS映射估计。此外，我们提出了一个内部共享单元（ISU）来改进NOCS地图估算。NOCS映射头具有三个分支。并且每个分支的估计坐标具有很强的相关性。在CAMERA和REAL数据集上的大量实验证明了联合优化在多任务类别级对象估计中的有效性。