Many possible use-cases for deep-learning-based 3D object recognition in logistic environments have been proposed . Nonetheless, the applicability of this technology in a logistic context has yet to be explored. Additionally, no dataset regarding point clouds in a logistic setting yet exists. Therefore, this paper investigates the generation of point clouds for logistics and the use of deep learning for classification and segmentation of the generated datasets. First, we generate different datasets regarding logistics with a virtual sensor. Afterwards, two state-of-the-art networks are evaluated and compared: PointNet++ and DGCN  . Three different tasks are considered: The classification of logistic objects under the closed-world as well as the open-set assumption is assessed. Finally, the segmentation of logistic scenes is evaluated. Additionally, since simple surfaces can be removed reliably by traditional means, the effect of removing these as pre- processing step is evaluated as well. Both networks are able to reliably classify logistic objects with and without floor, even in the presence of unknown classes. However, while the segmentation performs well on average, some negative outliers do exist. Using transfer learning by pretraining the network with point clouds presenting the complete shape leads to a better performance.
Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN
Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN.