Datasets:
license: cc-by-4.0
task_categories:
- object-detection
- keypoint-detection
- image-to-3d
language:
- en
pretty_name: Spacecraft Detection Keypoint and Pose Dataset
tags:
- spacecraft
- pose-estimation
- satellite
- computer-vision
- 6dof
Dataset Card: SDKP (Spacecraft Detection, Keypoint, and Pose Dataset)
Dataset Description
- Homepage: [Add project homepage or paper link here]
- License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Dataset Summary
The SDKP (Spacecraft Detection, Keypoint, and Pose) dataset is a comprehensive benchmark designed to advance spacecraft perception under monocular imaging conditions. It contains 24,000 high-quality RGB images, each accompanied by rich ground-truth annotations including 2D semantic keypoints, bounding boxes, and full 6-DoF poses. The data is split into three standardized subsets—20,000 for training, 2,000 for validation, and 2,000 for testing—ensuring consistent evaluation protocols. Organized in a COCO-compatible format, the dataset includes camera intrinsic parameters and a 3D keypoint template defining all semantic landmarks in the spacecraft body frame. This resource targets three core computer vision tasks: object detection, keypoint localization, and pose estimation, providing a unified platform for developing and comparing algorithms in spaceborne applications.
Dataset Structure
Data Instance
The dataset follows a COCO-compatible format. A typical data instance contains the following fields:
- Image: RGB image containing the spacecraft.
- Annotations:
bbox: Object detection bounding box.keypoints: Predefined 2D semantic keypoint coordinates with visibility flags.pose: Full 6-DoF pose (rotation matrix and translation vector).
- Metadata:
camera_intrinsics: Camera intrinsic parameter matrix.keypoint_template_3d: 3D template of all semantic keypoints in the spacecraft body frame.
Data Splits
| Split Name | Number of Images |
|---|---|
| Training | 20,000 |
| Validation | 2,000 |
| Test | 2,000 |
Supported Tasks
This dataset is designed for three core computer vision tasks, providing a unified platform for algorithm development and evaluation:
- Object Detection: Localize and identify spacecraft in images.
- Keypoint Localization: Precisely predict 2D pixel coordinates of predefined semantic keypoints on the spacecraft.
- Pose Estimation: Recover the 6-DoF pose of the spacecraft relative to the camera from a single RGB image.