论文标题
GEN6D：可从RGB图像中估算的无模型6-DOF对象姿势估计
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
论文作者
论文摘要
在本文中，我们提出了一个称为gen6d的可推广的无模型的6-DOF对象姿势估计器。现有的可推广姿势估计器要么需要高质量的对象模型，要么需要在测试时间内进行额外的深度图或对象掩码，这大大限制了其应用程序范围。相比之下，我们的姿势估计器仅需要一些看不见的对象的姿势图像，并且能够准确预测对象在任意环境中的姿势。 GEN6D由对象检测器，观点选择器和姿势炼油厂组成，所有这些都不需要3D对象模型，并且可以推广到看不见的对象。实验表明，Gen6D在两个无模型数据集上实现了最新的结果：我们收集的拖把数据集和一个新的GenMOP数据集。另外，在LineMod数据集上，Gen6D与实例特异性姿势估计器相比，获得了竞争性结果。项目页面：https：//liuyuan-pal.github.io/gen6d/。
In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D. Existing generalizable pose estimators either need high-quality object models or require additional depth maps or object masks in test time, which significantly limits their application scope. In contrast, our pose estimator only requires some posed images of the unseen object and is able to accurately predict the poses of the object in arbitrary environments. Gen6D consists of an object detector, a viewpoint selector and a pose refiner, all of which do not require the 3D object model and can generalize to unseen objects. Experiments show that Gen6D achieves state-of-the-art results on two model-free datasets: the MOPED dataset and a new GenMOP dataset collected by us. In addition, on the LINEMOD dataset, Gen6D achieves competitive results compared with instance-specific pose estimators. Project page: https://liuyuan-pal.github.io/Gen6D/.
