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/D-SPEED：面向时序航天器姿态估计的合成基准数据集 D-SPEED是一款专为基于深度学习的非合作航天器相对姿态估计任务打造的合成数据集，支持静态图像与视频两种输入形式。该数据集在现有数据集（SPEED、SPEED+）的基础上，新增了时序连贯的视频序列、精细化运动元数据，并采用虚幻引擎5（Unreal Engine 5）实现高分辨率渲染。 数据集包含以下内容： 1. 60000张搭载于不同姿态与光照条件下的探戈号（Tango）航天器高分辨率静态图像； 2. 21段视频序列（帧率25 FPS，每段含1500帧），涵盖11种不同运动轨迹（如交会对接、编队飞行、在轨巡视），并提供每帧的6自由度（6-DoF）姿态真值标签； 3. 内置相机内参以及预先划分的训练、验证、测试集，可支撑可复现的模型训练与评估流程。 相较于此前的数据集，D-SPEED通过连续视频流与带标注的运动事件（如加速度、相机/航天器运动），支持时序姿态估计算法的研发。 🎥 宣传视频：https://youtu.be/AbIYOj8LuNY 🛠 配套工具（支持2D关键点与边界框生成、可视化、真值标签生成以及基础评估流程）已在开源仓库中发布：👉 https://github.com/possoj/Spacecraft-Pose-Estimation-Framework 📄 如需了解运动轨迹元数据、采样分布以及视频序列生成流程的详细细节，请参阅相关论文（已投至《IEEE航空与电子系统汇刊》审稿中）与博士学位论文。 🎮 渲染工作由Rexys团队基于其虚幻引擎工具链完成：https://rexys.io 📝 若您使用本数据集，请引用以下文献： [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/