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

Human motion prediction is key to understand social environments, with direct
applications in robotics, surveillance, etc. We present a simple yet effective
pedestrian trajectory prediction model aimed at pedestrians positions
prediction in urban-like environments conditioned by the environment: map and
surround agents. Our model is a neural-based architecture that can run several
layers of attention blocks and transformers in an iterative sequential fashion,
allowing to capture the important features in the environment that improve
prediction. We show that without explicit introduction of social masks,
dynamical models, social pooling layers, or complicated graph-like structures,
it is possible to produce on par results with SoTA models, which makes our
approach easily extendable and configurable, depending on the data available.
We report results performing similarly with SoTA models on publicly available
and extensible-used datasets with unimodal prediction metrics ADE and FDE.

本文通过利用神经网络的注意力机制和迭代逐步学习的方式，结合环境因素预测市区环境下行人位置的运动，成功构建了一个简单、高效的行人轨迹预测模型，并证明了该模型在不需要引入实体掩码、动态模型、社交池层或类似图形结构方面，也能够与 SoTA 模型相媲美地达到相似的性能水平。