SDKP: A Synthetic Multi-Task Dataset for Monocular 6-DoF Pose Estimation of Non-Cooperative Spacecraft

Monocular six-degree-of-freedom (6-DoF) pose estimation of non-cooperative spacecraft is a fundamental technology for on-orbit servicing, space debris removal, and autonomous rendezvous and docking missions. Existing deep learning-based pose estimation methods typically rely on large-scale image datasets with accurate pose annotations for model training and evaluation. However, acquiring real on-orbit images is extremely expensive, while obtaining high-precision 6-DoF pose ground truth is even more challenging, significantly limiting the development and validation of related algorithms. To address these challenges, this work constructs and publicly releases SDKP (Spacecraft Detection, Keypoint and Pose Dataset), a synthetic multi-task dataset specifically designed for monocular pose estimation of non-cooperative spacecraft. SDKP is built on an automated physically based rendering pipeline with simulated multi-source on-orbit lighting conditions to produce 24,000 high-fidelity RGB images. All annotations are automatically generated within a unified coordinate framework to guarantee geometric consistency across multi-task labels. Each image is synchronously annotated within a self-defined JSON format, containing 2D/3D object detection bounding boxes, 2D projections of 3D semantic keypoints, and ground-truth 6-DoF pose annotations. During data generation, camera viewpoints are densely sampled within a spherical observation space, covering diverse azimuth angles, elevation angles, and observation distances to provide rich viewpoint variations. Meanwhile, both deep-space black backgrounds and Earth-background scenarios are incorporated to enhance environmental diversity. Ultimately, SDKP provides a unified annotation format and multi-level supervision information, aiming to serve as a standardized benchmark dataset for the development and evaluation of 2D/3D object detection, keypoint detection, and end-to-end pose estimation algorithms, while facilitating reproducible experiments and fair comparisons among different methods.