Tango Spacecraft Wireframe Dataset Model for Line Segments Detection

Reference Paper: M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369 M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS) General Description: The "Tango Spacecraft Wireframe Dataset Model for Line Segments Detection" dataset here published should be used for line detection and segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the only publicly available dataset of synthetic space-borne images tailored to line detection tasks (up to our knowledge). The label of each image gives the reprojection of a simplified wireframe model of Tango on the image plane split into lines. The labels are written following the Wireframe Model format. The "Tango Spacecraft Wireframe Dataset Model for Line Segments Detection" is also the largest dataset with wireframe annotations available up to date. More information on the dataset split and on the label format are reported below. Images Information: The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints. Labels Information: Labels in the Wireframe dataset format are here provided in separated JSON files. The files are formatted per each image as in the following example: width : 98 # width in pixels (int) of the current image height : 176 # height in pixels (int) of the current image lines : [[line1], [line2], ..., [lineN]] # list of lines in each image filename : tango_img_866.png # string with image name and format Per each line (line1, ... , lineN) in lines, the format is [x0, y0, x1, y1]. (x0, y0) are the coordinates (float) of the line starting point in the image reference frame (x pointing right and y pointing down with origin located in the top-left corner of the image). (X1, y1) are the coordinates (float) of the line ending point in the image reference frame (x pointing right and y pointing down with origin located in the top-left corner of the image). Note that the starting point is assumed to be the left-most endpoint (lower x coordinate in image reference frame) of each line. In the case of vertical lines, the starting point is the upper-most endpoint (lower y coordinate in image reference frame) of each line. VERSION CONTROL v1.0: All the images (both for train and test) have different resolutions, with Tango always centered in the image. The height of the images is in the range 19 - 352 pixels, while the width is in the range 16 - 336 pixels. The height over width ratio spans from 0.34 to 3.25. v2.0: This version contains all the images of v1.0 in the .zip folder named Tango_WF.zip, while in the .zip folder named Tango_WF_fullscale.zip there is the dataset (both train and test) of full scale images. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. The labels for the wireframe are in the same format of v1.0. Note: the dataset in v1.0 is obtained by cropping the fullscale images in v2.0 and by properly rescaling the wireframe annotations. Note: this dataset contains the same images of the "Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation" v1.0 (DOI: https://doi.org/10.5281/zenodo.6507863) and also "Tango Spacecraft Dataset for Monocular Pose Estimation" v1.0 (DOI: https://doi.org/10.5281/zenodo.6499007) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. These three datasets give the most comprehensive dataset of space borne synthetic images ever published (up to our knowledge).