In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. Operating in the range view involves well known challenges for learning, including occlusion and scale variation, but it also provides contextual information based on how the sensor data was captured. Our approach uses a fully convolutional network to predict a multimodal distribution over 3D boxes for each point and then it efficiently fuses these distributions to generate a prediction for each object. Experiments show that modeling each detection as a distribution rather than a single deterministic box leads to better overall detection performance. Benchmark results show that this approach has significantly lower runtime than other recent detectors and that it achieves state-of-the-art performance when compared on a large dataset that has enough data to overcome the challenges of training on the range view.
翻译:在本文中,我们展示了激光网,这是从LiDAR数据中自动驱动3D对象探测的一种计算高效方法。效率来自在输入数据自然紧凑的传感器本地射程视图中处理LiDAR数据的结果。在射程视图中操作涉及众所周知的学习挑战,包括隔离和比例变异,但也提供了基于传感器数据如何捕捉的背景资料。我们的方法使用一个完全革命的网络来预测每个点的3D框的多式联运分布,然后有效地结合这些分布,为每个对象产生预测。实验显示,将每个探测作为分布而不是单一确定性框进行模型,可以改进总体检测性能。基准结果显示,这种方法比其他最近的探测器运行时间要短得多,而且如果在一个拥有足够数据以克服射程视图培训挑战的大型数据集上,它将达到最先进的性性能。