论文标题
姿势建议和改进网络，用于更好的对象姿势估计
A Pose Proposal and Refinement Network for Better Object Pose Estimation
论文作者
论文摘要
在本文中，我们提出了一种在RGB输入上运行的新型端到端6D对象姿势估计方法。我们的方法由2个主要组成部分组成：第一个组件分类了输入图像中的对象，并通过基于多任务的基于CNN的Encoder/Multi-Decoder模块提出了初始6D姿势估算。第二个组件是改进模块，包括渲染器和多重意义的姿势改进网络，通过使用外观特征和流量向量，它迭代地完善了估计的姿势。我们的炼油厂利用了初始姿势估计的混合表示，以预测相对于目标姿势的相对误差。空间多发块进一步增强，强调对象的判别特征部分。对6D姿势估计的三个基准测试的实验表明，我们提出的管道优于具有竞争性运行时性能的最先进的基于RGB的方法。
In this paper, we present a novel, end-to-end 6D object pose estimation method that operates on RGB inputs. Our approach is composed of 2 main components: the first component classifies the objects in the input image and proposes an initial 6D pose estimate through a multi-task, CNN-based encoder/multi-decoder module. The second component, a refinement module, includes a renderer and a multi-attentional pose refinement network, which iteratively refines the estimated poses by utilizing both appearance features and flow vectors. Our refiner takes advantage of the hybrid representation of the initial pose estimates to predict the relative errors with respect to the target poses. It is further augmented by a spatial multi-attention block that emphasizes objects' discriminative feature parts. Experiments on three benchmarks for 6D pose estimation show that our proposed pipeline outperforms state-of-the-art RGB-based methods with competitive runtime performance.
