  Autonomous vehicles (AVs) heavily rely on LiDAR perception for environment
understanding and navigation. LiDAR intensity provides valuable information
about the reflected laser signals and plays a crucial role in enhancing the
perception capabilities of AVs. However, accurately simulating LiDAR intensity
remains a challenge due to the unavailability of material properties of the
objects in the environment, and complex interactions between the laser beam and
the environment. The proposed method aims to improve the accuracy of intensity
simulation by incorporating physics-based modalities within the deep learning
framework. One of the key entities that captures the interaction between the
laser beam and the objects is the angle of incidence. In this work we
demonstrate that the addition of the LiDAR incidence angle as a separate input
to the deep neural networks significantly enhances the results. We present a
comparative study between two prominent deep learning architectures: U-NET a
Convolutional Neural Network (CNN), and Pix2Pix a Generative Adversarial
Network (GAN). We implemented these two architectures for the intensity
prediction task and used SemanticKITTI and VoxelScape datasets for experiments.
The comparative analysis reveals that both architectures benefit from the
incidence angle as an additional input. Moreover, the Pix2Pix architecture
outperforms U-NET, especially when the incidence angle is incorporated.
