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Object Stacking Grasping Dataset (OSGD)

Background

    In task-oriented grasping, the robot is supposed to manipulate the objects in a task-compatible manner, which is more significant but more challenging than just stably grasping. More seriously, there are usually multiple stacked objects with serious overlaps and occlusions in real-world scenes.Therefore, we construct a synthetic dataset Object Stacking Grasping Dataset (OSGD) for task-oriented grasping in object stacking scenes.

Dataset Introduction

    To reduce data collection time, we construct the dataset OSGD by synthesis. This synthetic dataset contains 8000 depth maps of object stacking scenes. And for each object in the scene, we annotate its bounding box, category and task-compatible grasps, which is shown as follows:

 

                                                    

 

    The generation process of our dataset is introduced detailedly in paper "Task-oriented Grasping in Object Stacking Scenes with CRF-based Semantic Model".

    The model trained on dataset OSGD can be generalized to real-world scenes well. But it should be noted that, firstly, the object materials are not supposed to have strong specular reflection and light absorption. Secondly, the objects shouldn't be too small and too thin.

 

Dataset Format

    The dataset OSGD contains 4 folders, which are 'JPGEImages', 'Annotations', 'Grasps' and 'ImageSets'. 'JPEGImages' contains 8000 synthetic depth maps. 'Annotations' contains the object annotation files in which the objects' bounding boxes and categories are organized with .xml format. 'Grasps' contains the grasp annotation files in which each grasp is identified with its 4 vertexes, task label and the object it belongs to. 'ImageSets' contains the files which divide our dataset into a trainset and a testset which contains 7000 and 1000 depth maps respectively.

 

Dataset Download

     Downloading the dataset OSGD is free and open, please cite the paper "Task-oriented Grasping in Object Stacking Scenes with CRF-based Semantic Model". The download link is: https://www.dropbox.com/sh/ki4c5gokmzto6z1/AAAlWaKSJncq30RIjdYr6ezaa?dl=0

 

    The folder OSGD contains two compressed files OSGD_IMG.zip and OSGD_ORI.rar. In OSGD_IMG.zip, the depth maps are normalized to [0, 255] and stored with format .jpg. In OSGD_ORI.rar, the depth maps are stored with format .npy and the depth unit is dm.