We address the problem of estimating the relative 6D pose, i.e., position and
orientation, of a target spacecraft, from a monocular image, a key capability
for future autonomous Rendezvous and Proximity Operations. Due to the
difficulty of acquiring large sets of real images, spacecraft pose estimation
networks are exclusively trained on synthetic ones. However, because those
images do not capture the illumination conditions encountered in orbit, pose
estimation networks face a domain gap problem, i.e., they do not generalize to
real images. Our work introduces a method that bridges this domain gap. It
relies on a novel, end-to-end, neural-based architecture as well as a novel
learning strategy. This strategy improves the domain generalization abilities
of the network through multi-task learning and aggressive data augmentation
policies, thereby enforcing the network to learn domain-invariant features. We
demonstrate that our method effectively closes the domain gap, achieving
state-of-the-art accuracy on the widespread SPEED+ dataset. Finally, ablation
studies assess the impact of key components of our method on its generalization
abilities.

我们提出了一种通过多任务学习和积极的数据增强策略来改善网络的领域泛化能力，从而实现相对 6D 姿态和空间飞行器姿态估计的跨领域问题。

对于轨道 6D 位姿估计的领域泛化

Domain Generalization for In-Orbit 6D Pose Estimation

We introduce the new setting of open-vocabulary object 6D pose estimation, in
which a textual prompt is used to specify the object of interest. In contrast
to existing approaches, in our setting (i) the object of interest is specified
solely through the textual prompt, (ii) no object model (e.g. CAD or video
sequence) is required at inference, (iii) the object is imaged from two
different viewpoints of two different scenes, and (iv) the object was not
observed during the training phase. To operate in this setting, we introduce a
novel approach that leverages a Vision-Language Model to segment the object of
interest from two distinct scenes and to estimate its relative 6D pose. The key
of our approach is a carefully devised strategy to fuse object-level
information provided by the prompt with local image features, resulting in a
feature space that can generalize to novel concepts. We validate our approach
on a new benchmark based on two popular datasets, REAL275 and Toyota-Light,
which collectively encompass 39 object instances appearing in four thousand
image pairs. The results demonstrate that our approach outperforms both a
well-established hand-crafted method and a recent deep learning-based baseline
in estimating the relative 6D pose of objects in different scenes. Project
website: this https URL

我们在研究中引入了开放词汇的物体 6D 姿态估计新设置，其中使用文本提示来指定感兴趣的物体，并提出了一种新方法来通过视觉 - 语言模型从两个不同场景的图像中分割和估计物体的相对 6D 姿态。