SpaceSense-Bench: A Large-Scale Multi-Modal Benchmark for Spacecraft Perception and Pose Estimation
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间利用技术与工程中心）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Autonomous space operations such as on-orbit servicing and active debris removal demand robust part-level semantic understanding and precise relative navigation of target spacecraft, yet collecting large-scale real data in orbit remains impractical due to cost and access constraints. Existing synthetic datasets, moreover, suffer from limited target diversity, single-modality sensing, and incomplete ground-truth annotations. We present \textbf{SpaceSense-Bench}, a large-scale multi-modal benchmark for spacecraft perception encompassing 136~satellite models with approximately 70~GB of data. Each frame provides time-synchronized 1024$\times$1024 RGB images, millimeter-precision depth maps, and 256-beam LiDAR point clouds, together with dense 7-class part-level semantic labels at both the pixel and point level as well as accurate 6-DoF pose ground truth. The dataset is generated through a high-fidelity space simulation built in Unreal Engine~5 and a fully automated pipeline covering data acquisition, multi-stage quality control, and conversion to mainstream formats. We benchmark five representative tasks (object detection, 2D semantic segmentation, RGB--LiDAR fusion-based 3D point cloud segmentation, monocular depth estimation, and orientation estimation) and identify two key findings: (i)~perceiving small-scale components (\emph{e.g.}, thrusters and omni-antennas) and generalizing to entirely unseen spacecraft in a zero-shot setting remain critical bottlenecks for current methods, and (ii)~scaling up the number of training satellites yields substantial performance gains on novel targets, underscoring the value of large-scale, diverse datasets for space perception research. The dataset, code, and toolkit are publicly available at https://github.com/wuaodi/SpaceSense-Bench.
A New Dataset and Performance Benchmark for Real-time Spacecraft Segmentation in Onboard Computers
Department of Computer Science, Rice University（计算机科学系，里士大学）；Johnson Space Center, NASA（约翰逊航天中心，美国国家航空航天局）；Data to Knowledge Lab, Rice University（数据到知识实验室，里士大学）
European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry
KP Labs；Airbus Defence and Space GmbH（空客防务与航天有限公司）；Opole University of Technology（奥波莱理工大学）；Silesian University of Technology（西里西亚理工大学）；CGI Deutschland B.V. & Co. KG（CGI 德国有限两合公司）；European Space Operations Centre, European Space Agency（欧洲空间局欧洲空间操作中心）；Telespazio Germany GmbH（意大利空间公司德国有限公司）；LSE Space GmbH（LSE Space 有限公司）；Solenix GmbH（Solenix 有限公司）
SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
Earth System Science Center, University of Alabama in Huntsville, AL, USA（阿拉巴马大学亨茨维尔分校地球系统科学中心）；NASA Marshall Space Flight Center, Huntsville, AL, USA（美国国家航空航天局马歇尔航天飞行中心）；Department of Space Science, The University of Alabama in Huntsville, AL, USA（阿拉巴马大学亨茨维尔分校空间科学系）；Center for Space Plasma and Aeronomic Research (CSPAR), The University of Alabama in Huntsville, AL, USA（阿拉巴马大学亨茨维尔分校空间等离子体与航空研究中心）；IBM Research（IBM研究院）；Georgia State University（佐治亚州立大学）；Southwest Research Institute（西南研究院）；Princeton University（普林斯顿大学）；SETI Institute（搜寻地外文明研究所）；Laboratory for Atmospheric and Space Physics, University of Colorado Boulder（科罗拉多大学博尔德分校大气与空间物理实验室）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德空间飞行中心）
Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU
Thales Alenia Space Italia（泰雷兹阿莱尼亚航天意大利公司）
不合作空间驻留物体的位姿估计是近距离操作中实现自主性的关键资产。在这种情况下，单目相机是一种有价值的解决方案，因为它们的系统要求低。然而，相关联的图像处理算法对于实时的板上实现来说在计算上太昂贵，或者不够准确。在本文中，我们提出了一个姿态估计软件，利用神经网络架构，可以扩展到不同的精度-延迟权衡。我们设计了与边缘张量处理单元兼容的管道，以展示低功耗机器学习加速器如何在太空中实现人工智能开发。测试的神经网络的基准航天器姿态估计数据集，并特意开发的Cosmo真实感数据集，它描绘了一个COSMO-SkyMed卫星在各种随机姿势和可操纵的太阳能电池板的方向。我们架构的最轻版本在两个数据集上都实现了最先进的准确性，但网络复杂性只有一小部分，在Coral Dev Board Mini上以每秒7.7帧的速度运行，仅消耗2.2W。
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation
SnT, University of Luxembourg（卢森堡大学SnT分校）；Cristal Lab, ENSI, University of Manouba（曼努巴大学ENSI的Cristal实验室）；Infinite Orbits, Toulouse（图卢兹的Infinite Orbits）
SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision
Institute of Optics and Electronics, Chinese Academy of Sciences（中国科学院光电技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Australian National University（澳大利亚国立大学）
