Evaluation Dataset for Tracking Branched Deformable Linear Objects from Point Clouds

This dataset contains the evaluation data used in the PhD thesis *"Model-based Tracking of Branched Deformable Linear Objects from Point Clouds for Robotic Manipulation"* by Markus Wnuk, 2025, Technical University of Stuttgart, (to be submitted). It includes annotated 3D point clouds, kinematic configuration data, and metadata files that were used to quantitatively and qualitatively assess the proposed tracking method for Branched Deformable Linear Objects (BDLO).The dataset is intended to support reproducibility, benchmarking, and further research in the field of deformable object perception and robotic manipulation. ### Contents Background: RGB image of the scene background used for segmentation via background subtraction. BDLO Descriptions: Metadata describing each BDLO sample, including its graph structure (`model.json`) and label configuration (label IDs used for ground-truth reference). Camera Parameters: Intrinsic and extrinsic calibration data for the RGB-D camera used in the experiments. Evaluation Datasets: Scene-specific folders containing: Metadata (e.g., BDLO type, camera setup, grasping positions) RGB-D data (`.png` files for color and disparity) Results: Processed evaluation results, including: - Compact result files (`.pkl`) - Visualization plots (`.jpg`) ### Use Cases The dataset can be used for: - Benchmarking BDLO tracking and shape estimation methods- Developing perception pipelines for robotic manipulation of flexible or branched objects- Training and evaluation of learning-based or model-based approaches for deformable object localization ### Citation Please cite the following work if you use this dataset: > Markus Wnuk. (2025). *Model-based Tracking of Branched Deformable Linear Objects from Point Clouds for Robotic Manipulation* (PhD thesis). University of Stuttgart. DOI: 10.5281/zenodo.15665158 ### License This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to use, modify, and distribute the data, provided appropriate credit is given.