README.md

---
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:

1.  **Object Detection:** Localize and identify spacecraft in images.
2.  **Keypoint Localization:** Precisely predict 2D pixel coordinates of predefined semantic keypoints on the spacecraft.
3.  **Pose Estimation:** Recover the 6-DoF pose of the spacecraft relative to the camera from a single RGB image.