  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.
