The dataset is generated by rendering the 3D meshes of objects in the simulation Blender. The generated data will be further processed in python for data augmentation, in which the domain randomization techniques are applied to increase variaty. Along with the geneated anotations, the data will be used for training 6D pose estimation algorithms in Tensorflow.
Beschreibung:
The dataset includes the synthetic data generated from rendering the 3D meshes of LM objects in Blender for training 6D pose estimation algorithms. The whole dataset contains the synthetic data for 13 objects with 20,000 data samples for each object. Each data sample includes an RGB image in .png format and a depth image in .exr format. Each sample has the annotations of mask labels in .png format and the ground truth pose labels saved in .json files. Apart from the training data, the 3D meshes of the objects and the pre-trained models of the 6D pose estimation algorithm are also included. The whole dataset takes approximately 800 GB of storage memory.
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The dataset includes the synthetic data generated from rendering the 3D meshes of LM objects in Blender for training 6D pose estimation algorithms. The whole dataset contains the synthetic data for 13 objects with 20,000 data samples for each object. Each data sample includes an RGB image in .png format and a depth image in .exr format. Each sample has the annotations of mask labels in .png format and the ground truth pose labels saved in .json files. Apart from the training data, the 3D meshes...
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