---
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, 2D\3D 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`: 2D object bounding box (normalized YOLO format).
  - `bbox_3d`: 3D bounding box annotations, including 3D center, dimensions, yaw, depth, projected 2D center, and projected 2D vertices.
  - `keypoints_2d`: 2D semantic keypoint coordinates.
  - `camera_quaternion`: Ground-truth camera orientation (unit quaternion).
  - `camera_translation`: Ground-truth camera translation (meters).
- **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

The SDKP dataset is designed for multi-task spacecraft visual perception and supports the following computer vision tasks:

1. **2D/3D Object Detection:** Detect spacecraft in RGB images using both 2D and 3D bounding box annotations for object localization and spatial perception.

2. **2D Semantic Keypoint Detection:** Predict the image coordinates of predefined semantic keypoints on the spacecraft, establishing explicit correspondences between image observations and spacecraft structures.

3. **Monocular 6-DoF Pose Estimation:** Estimate the six-degree-of-freedom (6-DoF) pose of the spacecraft relative to the camera from a single RGB image using the provided ground-truth pose annotations.

## Annotation File Update

**Update Notice:**  
The initial release of the SDKP dataset did not include the **3D bounding box (`bbox_3d`) annotations** in the `annotation_train.json`, `annotation_val.json`, and `annotation_test.json` files.

To address this issue, we have uploaded the corrected annotation files in the **`Correction`** folder. These updated JSON files include the complete **3D bounding box (`bbox_3d`) annotations**, while all other annotation fields and image data remain unchanged.

Users are encouraged to use the corrected annotation files in the **`Correction`** folder for all future experiments involving the SDKP dataset.