Dynamic-Spacecraft Pose Estimation Dataset (D-SPEED)

D-SPEED: A Synthetic Benchmark for Temporal Spacecraft Pose Estimation D-SPEED is a synthetic dataset designed for deep learning-based relative pose estimation of non-cooperative spacecraft, from both still images and video. It extends prior datasets (SPEED, SPEED+) by introducing temporally coherent video sequences, detailed motion metadata, and high-resolution rendering using Unreal Engine 5. It contains: 60,000 high-resolution still images of the Tango spacecraft under varied poses and lighting. 21 video sequences (25 FPS, 1500 frames each) covering 11 distinct motion trajectories (e.g., docking, formation flying, inspection), with per-frame ground-truth 6-DoF poses. Camera intrinsics and predefined train/val/test splits to support reproducible training and evaluation. Compared to previous datasets, D-SPEED enables the development of temporal pose estimation algorithms through continuous video streams and annotated motion events (e.g., accelerations, camera/satellite motion). 🎥 Teaser video: https://youtu.be/AbIYOj8LuNY 🛠 Companion tools for 2D keypoint and bounding box generation, visualization, ground-truth generation, and basic evaluation workflows are available in the open-source repository:👉 https://github.com/possoj/Spacecraft-Pose-Estimation-Framework 📄 For trajectory metadata, sampling distributions, and details of the video sequence generation process, please refer to the associated paper (under review at IEEE Transactions on Aerospace and Electronic Systems) and the associated PhD thesis. 🎮 Rendering performed by Rexys using their Unreal Engine-based toolchain: https://rexys.io 📝 If you use this dataset, please cite: [1] Julien Posso, Guy Bois, and Yvon Savaria, Dynamic-Spacecraft Pose Estimation Dataset (D-SPEED), Zenodo, 2025. https://doi.org/10.5281/zenodo.15851302 [2] Julien Posso, Estimation de pose de véhicules spatiaux non coopératifs à partir de réseaux de neurones – De l'image monoculaire à l'implémentation embarquée temps réel et à l'analyse temporelle, PhD thesis, Polytechnique Montréal, 2025. https://publications.polymtl.ca/67849/