A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations
This addresses data scarcity for autonomous driving systems, but appears incremental as it builds on existing training methods.
The paper tackled the problem of expensive and limited annotated data for 3D object detection in autonomous driving by combining semi-pseudo-labeling and novel 3D augmentations, resulting in a method that significantly increases detection range compared to the training data distribution.
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.