zhixiang320/Spacecraft_Detection_Keypoint_and_Pose_Dataset

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

Supported Tasks

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

1. 1.2D/3D Object Detection: Detect spacecraft in RGB images using both 2D and 3D bounding box annotations for object localization and spatial perception.

1. 1.2D Semantic Keypoint Detection: Predict the image coordinates of predefined semantic keypoints on the spacecraft, establishing explicit correspondences between image observations and spacecraft structures.

1. 1.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.