SPE3R: Synthetic Dataset for Satellite Pose Estimation and 3D Reconstruction

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Abstract/Contents

Abstract: 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
Date created	December 13, 2023
Date modified	January 17, 2024; March 18, 2024
Publication date	January 4, 2024; January 4, 2024

Author	Park, Tae Ha	https://orcid.org/0000-0001-7938-9976 (unverified)
Author	D'Amico, Simone

Subject	Deep learning (Machine learning)
Subject	Pose Estimation
Subject	3D Reconstruction
Subject	Satellites
Subject	Orbital rendezvous (Space flight)
Genre	Image
Genre	3d model
Genre	Remote sensing imagery
Genre	Three-dimensional scan
Genre	Remote-sensing images
Genre	Dataset

Related item	Preferred citation Park, T. H. and D'Amico, S. Rapid Abstraction of Spacecraft 3D Structure from Single 2D Image. AIAA SCITECH 2024 Forum (2024). DOI: 10.2514/6.2024-2768.
DOI	https://doi.org/10.25740/pk719hm4806
Location	https://purl.stanford.edu/pk719hm4806

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.
License: This work is licensed under a Creative Commons Attribution Non Commercial Share Alike 4.0 International license (CC BY-NC-SA).

Preferred citation: Park, T. H. and D'Amico, S. (2024). SPE3R: Synthetic Dataset for Satellite Pose Estimation and 3D Reconstruction. Stanford Digital Repository. Available at https://purl.stanford.edu/pk719hm4806. https://doi.org/10.25740/pk719hm4806.

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