  Advances in lidar technology have made the collection of 3D point clouds fast
and easy. While most lidar sensors return per-point intensity (or reflectance)
values along with range measurements, flash lidar sensors are able to provide
information about the shape of the return pulse. The shape of the return
waveform is affected by many factors, including the distance that the light
pulse travels and the angle of incidence with a surface. Importantly, the shape
of the return waveform also depends on the material properties of the
reflecting surface. In this paper, we investigate whether the material type or
class can be determined from the full-waveform response. First, as a proof of
concept, we demonstrate that the extra information about material class, if
known accurately, can improve performance on scene understanding tasks such as
semantic segmentation. Next, we learn two different full-waveform material
classifiers: a random forest classifier and a temporal convolutional neural
network (TCN) classifier. We find that, in some cases, material types can be
distinguished, and that the TCN generally performs better across a wider range
of materials. However, factors such as angle of incidence, material colour, and
material similarity may hinder overall performance.
