A large-scale, physically-based synthetic dataset for satellite pose estimation
This addresses the domain gap for autonomous spacecraft operations in proximity and servicing missions, but is incremental as it builds on existing synthetic data methods.
The paper tackles the problem of satellite pose estimation by introducing the DLVS3-HST-V1 synthetic dataset, which uses advanced rendering to generate large-scale, annotated images with ground-truth data, enabling training and benchmarking under realistic conditions.
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.