用物理仿真生成卫星位姿数据集，提升航天器自主对接精度。
A large-scale, physically-based synthetic dataset for satellite pose estimation
- 基于物理渲染生成高保真合成图像，支持实时与离线渲染。
- 包含6自由度位姿、关键点等多维标注，覆盖复杂光照条件。
- 适合训练航天器自主导航与在轨维护的深度学习模型。
深度学习视觉空间模拟系统（DLVS3）提出一种专用于卫星位姿估计训练与测试的合成数据集生成器与仿真流程。本文介绍了聚焦哈勃太空望远镜（HST）这一复杂可动目标的DLVS3-HST-V1数据集。该数据集采用先进实时与离线渲染技术，融合高保真3D模型、动态光照（含地球反射等次级光源）及物理准确材质属性。仿真流程可生成大规模、多标签图像集，包含真实位姿（6-DoF）、关键点、语义分割、深度图与法向图。该数据集支持在逼真、多样且具有挑战性的视觉条件下训练与评估深度学习位姿估计方法。论文详细描述了数据生成过程、仿真架构及其与深度学习框架的集成，定位为缩小自主航天器近距离作业与在轨服务任务中领域差距的重要一步。
原文摘要 · Abstract (English)
The Deep Learning Visual Space Simulation System (DLVS3) introduces a novel synthetic dataset generator and a simulation pipeline specifically designed for training and testing satellite pose estimation solutions. This work introduces the DLVS3-HST-V1 dataset, which focuses on the Hubble Space Telescope (HST) as a complex, articulated target. The dataset is generated using advanced real-time and offline rendering technologies, integrating high-fidelity 3D models, dynamic lighting (including secondary sources like Earth reflection), and physically accurate material properties. The pipeline supports the creation of large-scale, richly annotated image sets with ground-truth 6-DoF pose and keypoint data, semantic segmentation, depth, and normal maps. This enables the training and benchmarking of deep learning-based pose estimation solutions under realistic, diverse, and challenging visual conditions. The paper details the dataset generation process, the simulation architecture, and the integration with deep learning frameworks, and positions DLVS3 as a significant step toward closing the domain gap for autonomous spacecraft operations in proximity and servicing missions.
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