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.

本文提出了 LaserNet，一种 3D 物体检测的计算有效方法，它通过在传感器的本地范围视图中处理 LiDAR 数据来提高效率，并使用全卷积网络来预测每个点的 3D 框的多模式分布，然后高效地融合这些分布以生成每个对象的预测，实验证明这种方法比其他方法具有显著更低的运行时间，并且在大型数据集上具有比其他检测器更好的性能。