Practical object pose estimation demands robustness against occlusions to the target object. State-of-the-art (SOTA) object pose estimators take a two-stage approach, where the first stage predicts 2D landmarks using a deep network and the second stage solves for 6DOF pose from 2D-3D correspondences. Albeit widely adopted, such two-stage approaches could suffer from novel occlusions when generalising and weak landmark coherence due to disrupted features. To address these issues, we develop a novel occlude-and-blackout batch augmentation technique to learn occlusion-robust deep features, and a multi-precision supervision architecture to encourage holistic pose representation learning for accurate and coherent landmark predictions. We perform careful ablation tests to verify the impact of our innovations and compare our method to SOTA pose estimators. Without the need of any post-processing or refinement, our method exhibits superior performance on the LINEMOD dataset. On the YCB-Video dataset our method outperforms all non-refinement methods in terms of the ADD(-S) metric. We also demonstrate the high data-efficiency of our method. Our code is available at http://github.com/BoChenYS/ROPE
翻译:对目标对象的封闭性进行强力估计。 状态- 艺术( SOTA) 对象显示, 估计者采取两阶段方法, 第一阶段预测使用深网络的2D里程碑, 6DF 的第二阶段解决方案来自2D-3D 对应的2D-3D通信。 尽管广泛采用, 这种两阶段方法在概括性时可能会受到新颖的封闭性的影响, 并且由于特征的中断而导致的里程碑性一致性薄弱。 为了解决这些问题, 我们开发了一种新的 occlude- and-blackout 批量增强技术, 以学习 clusion-robust 深度特征, 以及多精密监督结构, 以鼓励以整体形式学习用于准确和连贯的标志性预测的2DOF 标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志性标志