Existing learning-based methods for object pose estimation in RGB images are mostly model-specific or category based. They lack the capability to generalize to new object categories at test time, hence severely hindering their practicability and scalability. Notably, recent attempts have been made to solve this issue, but they still require accurate 3D data of the object surface at both train and test time. In this paper, we introduce a novel approach that can estimate in a single forward pass the pose of objects never seen during training, given minimum input. In contrast to existing state-of-the-art approaches, which rely on task-specific modules, our proposed model is entirely based on a transformer architecture, which can benefit from recently proposed 3D-geometry general pretraining. We conduct extensive experiments and report state-of-the-art one-shot performance on the challenging LINEMOD benchmark. Finally, extensive ablations allow us to determine good practices with this relatively new type of architecture in the field.

翻译：现有基于学习的RGB图像物体姿态估计方法大多针对特定模型或类别，缺乏在测试时泛化至新物体类别的能力，严重限制了其实用性与可扩展性。值得注意的是，近期虽有研究尝试解决此问题，但仍需在训练和测试阶段获取精确的物体表面三维数据。本文提出一种新方法，可在单次前向传播中通过最少输入估计训练中未见物体的姿态。与依赖任务特定模块的现有最先进方法不同，本模型完全基于Transformer架构，可利用近期提出的三维几何通用预训练技术。我们通过大量实验，在具有挑战性的LINEMOD基准上展示了最先进的单次性能。最后，广泛的消融研究帮助我们确立了该领域内这类新型架构的实践准则。