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Energy-saving LIDAR camera for short distances estimates an object's distanceusing temporally intensity-coded laser light pulses and calculates the maximumcorrelation with the back-scattered pulse.  Though on low power, the backs-scattered pulse is noisy and unstable, whichleads to inaccurate and unreliable depth estimation.  To address this problem, we use GANs (Generative Adversarial Networks), whichare two neural networks that can learn complicated class distributions throughan adversarial process. We learn the LIDAR camera's hidden properties andbehavior, creating a novel, fully unsupervised forward model that simulates thecamera. Then, we use the model's differentiability to explore the cameraparameter space and optimize those parameters in terms of depth, accuracy, andstability. To achieve this goal, we also propose a new custom loss functiondesignated to the back-scattered code distribution's weaknesses and itscircular behavior. The results are demonstrated on both synthetic and realdata.

SimuGAN: Unsupervised forward modeling and optimal design of a LIDAR Camera