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.

通过物理学模拟，提出一种蒙特卡罗混合方法用于模拟恶劣天气下的 LiDAR 点云数据，从而扩展所有天气条件下目标检测器的可靠性，并表明相对于现有模型，在现实世界的雨天场景中，模型的性能有了显著改进。

Lidar 光散射增强（LISA）：基于物理的 3D 物体检测的不良天气条件模拟

Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of  Adverse Weather Conditions for 3D Object Detection

A restricted Boltzmann machine is a generative probabilistic graphic network.
A probability of finding the network in a certain configuration is given by the
Boltzmann distribution. Given training data, its learning is done by optimizing
parameters of the energy function of the network. In this paper, we analyze the
training process of the restricted Boltzmann machine in the context of
statistical physics. As an illustration, for small size Bar-and-Stripe
patterns, we calculate thermodynamic quantities such as entropy, free energy,
and internal energy as a function of training epoch. We demonstrate the growth
of the correlation between the visible and hidden layers via the subadditivity
of entropies as the training proceeds. Using the Monte-Carlo simulation of
trajectories of the visible and hidden vectors in configuration space, we also
calculate the distribution of the work done on the restricted Boltzmann machine
by switching the parameters of the energy function. We discuss the Jarzynski
equality which connects the path average of the exponential function of the
work and the difference in free energies before and after training.

本文分析了受限玻尔兹曼机在统计物理上的训练过程，以小的条纹图案为例，计算了在训练的过程中信息熵、自由能和内能的变化以及可见层和隐藏层之间的互相关性增长，并使用蒙特卡洛模拟计算了能量函数的参数变化对受限玻尔兹曼机所做工的分布，并探讨了 Jarzynski 等式和训练前后自由能差的路径平均指数函数之间的关系。