ASTRA: A Large-Scale Multi-Satellite Benchmark for Onboard 6-DoF Spacecraft Pose Estimation

Accurate six-degree-of-freedom (6-DoF) pose estimation of non-cooperative satellites is critical for on-orbit servicing, assembly, and manufacturing missions, yet progress is bottlenecked by a severe scarcity of labeled in-orbit imagery and by the narrow target coverage of existing public datasets, most of which focus on only one or two spacecraft. We introduce ASTRA, a large-scale multi-satellite benchmark for onboard 6-DoF pose estimation that spans twenty structurally diverse targets drawn from NASA reference CAD models. ASTRA pairs every target with two complementary data regimes under a common annotation format, comprising an independent-frame training subset rendered under broad parameter sweeps for diverse pose coverage and a sequential test subset of rendezvous trajectories. To close the sim-to-real gap without relying on physical hardware-in-the-loop (HIL) facilities, ASTRA is released with two orthogonal real-style generation pipelines. FRESCO is a neural operator that performs band-selective Fourier amplitude mapping while preserving pose-critical geometry, and a world-model pipeline based on NVIDIA COSMOS turns sequential edge maps from synthetic renderings into photorealistic frames with diverse space environments.