{"url":"/dataset/data-for-image-based-backbone-reconstruction","name":"Data for \"Image-based Backbone Reconstruction for Non-Slender Soft Robots\"","full_name":null,"description_markdown":"# Data for \"Image-based Backbone Reconstruction for Non-Slender Soft Robots\"\r\n\r\nThis dataset provides the data for the forthcoming paper \r\n\"Image-based Backbone Reconstruction for Non-Slender Soft Robots\".\r\nThe backbone reconstruction method used is based on the method described in Hoffmann et al. [1]. The modifications to this method to support the non-slender soft robot in this dataset are described in the forthcoming paper mentioned above.\r\nThis dataset holds raw images of pressurized and elongated soft robots and the corresponding reconstructed backbones.\r\n\r\n## Dataset\r\n\r\nThe dataset is split into two subsets with similar structure. The first subset is contained in `dataset_01`. The second dataset is contained in `dataset_02`.\r\n\r\nEach subset consists of five folders and one schedule file.\r\nThe schedule file `schedule.csv` contains the index of the schedule entry, the angle $\\alpha$ in degree, the pressure of each chamber $p_1$ to $p_3$ in bar and if the pressurization is active.\r\nFurthermore, the five folders of the subset can be described as follows\r\n\r\n- `raw`: Contains the raw cropped images. The filenames are formatted as `CROPPED_C{CAMERA_INDEX}_E{SCHEDULE_ENTRY}.png` with the camera index `CAMERA_INDEX` and the schedule entry `SCHEDULE_ENTRY`.\r\n\r\n- `constant_curvature_slender`, `constant_curvature_volumetric`, `cubic_curvature_slender` and `cubic_curvature_volumetric`. These folders contain the actual reconstructed backbones based on the raw data from the `raw` folder. A different reconstruction approach was used in each of these folders\r\n    - `constant_curvature_slender` - A constant curvature backbone kinematic based on the slender model,\r\n    - `constant_curvature_volumetric` - A constant curvature backbone kinematic based on the volumetric model,\r\n    - `cubic_curvature_slender` - A cubic curvature backbone kinematic based on the slender model,\r\n    - `cubic_curvature_volumetric` -  A cubic curvature backbone kinematic based on the volumetric model.\r\n\r\n    Each of these folders contain a `data` and `figures` folder. The data folder consists of `PARAMETER_E{SCHEDULE_ENTRY}.json` files listing the optimization parameters for each schedule entry `SCHEDULE_ENTRY` in the JSON format.\r\n    The `figures` folder contains annotated images of the reconstructed backbone on the cropped raw images. The filenames are structured `ANNOTATED_E{SCHEDULE_ENTRY}_C{CAMERA_INDEX}_EPOCH{EPOCH}.png` with the schedule entry `SCHEDULE_ENTRY`, the camera index `CAMERA_INDEX` and the epoch `EPOCH` of the optimization algorithm.\r\n\r\nThe optimization parameters include the base position `base_position` of the reconstructed backbone in world coordinates, the coefficients for the curvature polynomials `ux` and `uy`, and the constant coefficient for the elongation polynomial `la`.\r\n\r\n## Calibration Data\r\n\r\nThe calibration data is located in the `calibration` folder and consists of multiple `.npy` files in the numpy format. The corresponding camera index for the calibrated camera is abbreviated with `CAMERA_INDEX` in the following:\r\n\r\n- `C{CAMERA_INDEX}.npy` - Stores the reprojection error, camera matrix, distortion coefficients, rotation, and translation vectors as returned by the `cv2.calibrateCamera` [2] method. \r\n- `C{CAMERA_INDEX}_camera_matrix.npy` - Stores the camera_matrix as returned by the `cv2.calibrateCamera` [2] method. \r\n- `C{CAMERA_INDEX}_distortion_coefficients.npy` - Stores the distortion coefficients as returned by the `cv2.calibrateCamera` [2] method. \r\n- `C{CAMERA_INDEX}_projection_matrix.npy` - Stores the projection matrix from world space to pixel space based on the stereo camera calibration.\r\n- `STEREO.npy` - Stores the reprojection error, R, T, E, F as returned by the `cv2.stereoCalibrate` [2] method as an object datatype. \r\n\r\n## Acknowledgement\r\n\r\nFunded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 501861263 – SPP2353\r\n\r\n## References\r\n\r\n[1] M. K. Hoffmann, J. Mühlenhoff, Z. Ding, T. Sattel and K. Flaßkamp. An iterative closest point algorithm for marker-free 3D shape registration of continuum robots. arXiv.\r\nhttps://arxiv.org/abs/2405.15336\r\n\r\n[2] OpenCV. Camera Calibration and 3D Reconstruction. OpenCV Documentation. https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html, accessed May 27, 2024.","description_withheld":null,"homepage":"https://zenodo.org/records/11352739","introduced_date":"2024-05-28","introduced_date_note":null,"introduced_by":{"paper":"/paper/image-based-backbone-reconstruction-for-non","title":"Image-based backbone reconstruction for non-slender soft robots","first_author":"Leon Schindler","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode.en"},"modalities":[{"name":"Tracking","url":"/datasets/modality/tracking"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Data for \"Image-based Backbone Reconstruction for Non-Slender Soft Robots\""],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}