SPE3R: Synthetic Dataset for Satellite Pose Estimation and 3D Reconstruction
- This repository contains the Satellite Pose Estimation and 3D Reconstruction (SPE3R) dataset comprising 64 unique spacecraft 3D models. The models are selectively acquired from the NASA 3D Resources and ESA Science Satellite Fleet. Each of these models are normalized and made watertight, and they are accompanied by 1,000 images, binary masks and corresponding pose labels in order to support simultaneous 3D structure characterization and pose estimation. The images and binary masks are rendered using a custom high-fidelity synthetic scene constructed in Unreal Engine. The dataset is divided up into training, validation and test sets, such that the validation set is used to evaluate an algorithm's generalization capability on unseen images of known targets (i.e., seen during training), whereas the test set helps evaluate it on images of unknown targets (i.e., unseen during training).
|Type of resource
|still image, three dimensional object, Dataset
|December 13, 2023
|January 17, 2024
|January 4, 2024; January 4, 2024
|Park, Tae Ha
|Deep learning (Machine learning)
|Orbital rendezvous (Space flight)
|Remote sensing imagery
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- This work is licensed under a Creative Commons Attribution Non Commercial Share Alike 4.0 International license (CC BY-NC-SA).
Stanford Research DataView other items in this collection in SearchWorks
Also listed in
Loading usage metrics...