REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5_5. Interactive perception. Interactive Labeling of Deformable Linear Objects. v0

The dataset contains the source code, model weights and a set of input points, camera poses and images utilized for the experimental validation on the labeling of deformable linear objects, associated to a novel algorithm called DLO-WSL. The proposed approach uses deep learning techniques aiming at the precise creation of instance masks of deformable linear objects starting from the input points provided by a user. The source code comprises a deep convolutional neural network employed for computing the correction offset to be applied at the input points. The dataset is associated with the related publication: A. Caporali, M. Pantano, L. Janisch, D. Regulin, G. Palli and D. Lee, "A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects," in IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 1013-1020, Feb. 2023, doi: 10.1109/LRA.2023.3234799.

本数据集包含用于可变形线性物体（deformable linear objects, DLO）标注实验验证的源代码、模型权重、一组输入点、相机位姿及图像，关联一款名为DLO-WSL的新型算法。所提出的方法采用深度学习技术，旨在基于用户提供的输入点精准生成可变形线性物体的实例掩码。该源代码包含一个深度卷积神经网络，用于计算需施加于输入点的校正偏移量。本数据集关联的相关学术论文为：A. Caporali、M. Pantano、L. Janisch、D. Regulin、G. Palli与D. Lee，题为《面向可变形线性物体的弱监督半自动图像标注方法》（A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects），发表于IEEE Robotics and Automation Letters，2023年2月，第8卷第2期，页码1013-1020，DOI：10.1109/LRA.2023.3234799。