GAS (Grasp Area Segmentation)
Introduced by Kaynar et al. in Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot Learning
GAS (Grasp Area Segmentation) dataset consists of 10089 RGB images of cluttered scenes grouped into 1121 grasp-area segmentation tasks. For each RGB image we provide a binary segmentation map with the graspable and non-graspable regions for every object in the scene. The dataset can be used for meta-training part-based grasp area estimation networks.
For creating the GAS dataset we use the RGB images and corresponding ground truth segmentation masks from the GraspNet 1-Billion dataset.
Papers
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