  Training neural networks to perform 3D object detection for autonomous
driving requires a large amount of diverse annotated data. However, obtaining
training data with sufficient quality and quantity is expensive and sometimes
impossible due to human and sensor constraints. Therefore, a novel solution is
needed for extending current training methods to overcome this limitation and
enable accurate 3D object detection. Our solution for the above-mentioned
problem combines semi-pseudo-labeling and novel 3D augmentations. For
demonstrating the applicability of the proposed method, we have designed a
convolutional neural network for 3D object detection which can significantly
increase the detection range in comparison with the training data distribution.
