论文标题
SC6D：对称性和无信号的6D对象姿势估计
SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation
论文作者
论文摘要
本文介绍了一个有效的对称性和无对应框架，称为SC6D，对于单个单眼RGB图像的6D对象姿势估计。 SC6D既不需要对象的3D CAD模型，也不需要对称对称性的任何先验知识。姿势估计分解为三个子任务：a）对象3D旋转表示学习和匹配； b）估计对象中心的2D位置； c）通过分类估计距离距离估计（沿Z轴的翻译）。 SC6D在三个基准数据集（T-less，YCB-V和ITODD）上进行评估，并在T-less数据集中获得最先进的性能。此外，SC6D在计算上比以前的最新方法Surfemb更有效。实施和预培训模型可在https://github.com/dingdingcai/sc6d-pose上公开获得。
This paper presents an efficient symmetry-agnostic and correspondence-free framework, referred to as SC6D, for 6D object pose estimation from a single monocular RGB image. SC6D requires neither the 3D CAD model of the object nor any prior knowledge of the symmetries. The pose estimation is decomposed into three sub-tasks: a) object 3D rotation representation learning and matching; b) estimation of the 2D location of the object center; and c) scale-invariant distance estimation (the translation along the z-axis) via classification. SC6D is evaluated on three benchmark datasets, T-LESS, YCB-V, and ITODD, and results in state-of-the-art performance on the T-LESS dataset. Moreover, SC6D is computationally much more efficient than the previous state-of-the-art method SurfEmb. The implementation and pre-trained models are publicly available at https://github.com/dingdingcai/SC6D-pose.
