  Lidar-based object detectors are critical parts of the 3D perception pipeline
in autonomous navigation systems such as self-driving cars. However, they are
known to be sensitive to adverse weather conditions such as rain, snow and fog
due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio
(SBR). As a result, lidar-based object detectors trained on data captured in
normal weather tend to perform poorly in such scenarios. However, collecting
and labelling sufficient training data in a diverse range of adverse weather
conditions is laborious and prohibitively expensive. To address this issue, we
propose a physics-based approach to simulate lidar point clouds of scenes in
adverse weather conditions. These augmented datasets can then be used to train
lidar-based detectors to improve their all-weather reliability. Specifically,
we introduce a hybrid Monte-Carlo based approach that treats (i) the effects of
large particles by placing them randomly and comparing their back reflected
power against the target, and (ii) attenuation effects on average through
calculation of scattering efficiencies from the Mie theory and particle size
distributions. Retraining networks with this augmented data improves mean
average precision evaluated on real world rainy scenes and we observe greater
improvement in performance with our model relative to existing models from the
literature. Furthermore, we evaluate recent state-of-the-art detectors on the
simulated weather conditions and present an in-depth analysis of their
performance.
