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DoPose: dataset for object segmentation and 6D pose estimation

Authors: Gouda, Anas; Nedungadi, Ashwin; Ghatpande, Anay; Reining, Christopher; Youssef, Hazem; Roidl, Moritz;

DoPose: dataset for object segmentation and 6D pose estimation

Abstract

DoPose (Dortmund Pose)is a dataset of highly cluttered and closely stacked objects. The dataset is saved in the BOP format. The dataset includes RGB images, Depth images, 6D Pose of objects, segmentation mask (all and visible), COCO Json annotation, camera transformations, and 3D model of all objects. The dataset contains 2 different types of scenes (table and bin). Each scene contains different view angles. For the bin scenes, the data contains 183 scenes with 2150 image views. In those 183 scenes 35 scenes contain 2 views, 20 contains 3 views and 128 contains 16 views. And for table scenes, the data contains 118 scenes with 1175 image views. in Those 118 scenes, 20 scenes contain 3 views, 50 scenes with 6 images, and 48 scenes with 17 images. So in total, our data contains 301 scenes and 3325 view images. Most of the scenes contain mixed objects. The dataset contains 19 objects in total. For more info about the dataset content and collection process please refer to our Arxiv preprint If you have any questions about the dataset, please contact anas.gouda@tu-dortmund.de

Acknowledgement: This work is funded by ML2R – The Competence Center Machine Learning Rhine-Ruhr which is funded by the Federal Ministry of Education and Research of Germany (grant no. 01|S18038A).

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Keywords

object segmentation, object detection, 6D pose estimation, household objects

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