Meanwhile, all szybkie of standard 3D convolutions are replaced with depthwise separable convolutions in the LVNet so as to reduce the model size and computation complexity. Rent this article szybkie via DeepDyve..
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Rent this article szybkie via DeepDyve.. . . The 3D object recognition has become one of hot topics in computer vision with the increasing of application scenarios of 3D data, such as robotic systems, autonomous driving, and security check systems using active millimeter wave. Although 3D convolutional neural network (CNN) has achieved some good results in 3D object recognition, its key performances such as computational efficiency and realtimeness still need to be improved due to its huge amount of parameters of 3D convolutions. In this paper, we present a powerful tool LVNet which is a lightweight volumetric CNN designed for real-time and high-performance recognition of 3D objects. In order to further improve the performance of LVNet, some auxiliary methods are employed also, such as data augmentation with multiple rotations of objects and information fusion of different sts szybkie wypłaty orientations. Furthermore, the attention mechanism is combined with the depthwise separable convolutions to compensate for the performance loss caused by the reduction of parameter number. In order to further improve the performance of LVNet, some auxiliary methods are employed also, such as data augmentation with multiple rotations of objects and information fusion of different orientations. A series of experimental results on public datasets show that the proposed LVNet achieves competitive recognition performance with less burden of computation and memory. Kartki na dzień dobry do pobrania za darmo.. This is a preview of subscription szybkie content, log in via an institution to check access.
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