论文标题
零星：强化场景级别的物体姿势完善的合理性
SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement
论文作者
论文摘要
观察噪声，不准确的分割和由于对称性和遮挡引起的歧义导致对象不准确姿势估计。虽然基于深度和RGB的姿势细化方法提高了所得姿势估计的准确性，但由于它们考虑视觉对齐，它们在观察过程中容易受到歧义。我们建议利用我们经常观察到静态，僵化的场景的事实。因此，其中的对象需要在物理上合理的姿势下。我们表明，考虑到合理性会降低歧义，因此，在混乱的环境中可以更准确地预测姿势。为此，我们将最新的基于RL的注册方法扩展到对物体姿势的迭代完善。 LineMod和YCB-Video数据集的实验证明了我们基于深度的精炼方法的最新性能。
Observational noise, inaccurate segmentation and ambiguity due to symmetry and occlusion lead to inaccurate object pose estimates. While depth- and RGB-based pose refinement approaches increase the accuracy of the resulting pose estimates, they are susceptible to ambiguity in the observation as they consider visual alignment. We propose to leverage the fact that we often observe static, rigid scenes. Thus, the objects therein need to be under physically plausible poses. We show that considering plausibility reduces ambiguity and, in consequence, allows poses to be more accurately predicted in cluttered environments. To this end, we extend a recent RL-based registration approach towards iterative refinement of object poses. Experiments on the LINEMOD and YCB-VIDEO datasets demonstrate the state-of-the-art performance of our depth-based refinement approach.
