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).

🎮 Rendering performed by Rexys using their Unreal Engine-based toolchain: https://rexys.io

📝 If you use this dataset, please cite:
Julien Posso, Guy Bois, and Yvon Savaria, Dynamic-Spacecraft Pose Estimation Dataset (D-SPEED), Zenodo, 2025.
https://doi.org/10.5281/zenodo.15851302