论文标题
一种新型的神经网络训练方法，用于使用半伪标签和3D数据增强的自动驾驶
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations
论文作者
论文摘要
培训神经网络以执行3D对象检测进行自主驾驶需要大量不同的注释数据。但是，以足够的质量和数量获取培训数据是昂贵的，有时由于人类和传感器的限制是不可能的。因此，需要一种新的解决方案来扩展当前训练方法以克服此限制并实现准确的3D对象检测。我们针对上述问题的解决方案结合了半伪标记和新颖的3D增强。为了证明所提出的方法的适用性，我们为3D对象检测设计了一个卷积神经网络，与训练数据分布相比，可以显着增加检测范围。
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.
