| --- |
| 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) |
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| ## Dataset Description |
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| - **Homepage:** [Add project homepage or paper link here] |
| - **License:** Creative Commons Attribution 4.0 International (CC BY 4.0) |
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| ### Dataset Summary |
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| 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. |
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| ## Dataset Structure |
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| ### Data Instance |
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| The dataset follows a COCO-compatible format. A typical data instance contains the following fields: |
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| - **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. |
|
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| ### Data Splits |
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| | Split Name | Number of Images | |
| | :--- | :--- | |
| | **Training** | 20,000 | |
| | **Validation** | 2,000 | |
| | **Test** | 2,000 | |
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| ## Supported Tasks |
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| The SDKP dataset is designed for multi-task spacecraft visual perception and supports the following computer vision tasks: |
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| 1. **2D/3D Object Detection:** Detect spacecraft in RGB images using both 2D and 3D bounding box annotations for object localization and spatial perception. |
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| 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. |
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| 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. |
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| ## Annotation File Update |
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| **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. |
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| Users are encouraged to use the corrected annotation files in the **`Correction`** folder for all future experiments involving the SDKP dataset. |
