  Energy-saving LIDAR camera for short distances estimates an object's distance
using temporally intensity-coded laser light pulses and calculates the maximum
correlation with the back-scattered pulse.
  Though on low power, the backs-scattered pulse is noisy and unstable, which
leads to inaccurate and unreliable depth estimation.
  To address this problem, we use GANs (Generative Adversarial Networks), which
are two neural networks that can learn complicated class distributions through
an adversarial process. We learn the LIDAR camera's hidden properties and
behavior, creating a novel, fully unsupervised forward model that simulates the
camera. Then, we use the model's differentiability to explore the camera
parameter space and optimize those parameters in terms of depth, accuracy, and
stability. To achieve this goal, we also propose a new custom loss function
designated to the back-scattered code distribution's weaknesses and its
circular behavior. The results are demonstrated on both synthetic and real
data.
