Abstract: The present disclosure generally pertains to time-of-flight simulation data training circuitry, configured to: obtain time-of-flight camera model data based on a time-of-flight camera model modelling a real time-of-flight camera of a real time-of-flight camera type; obtain real time-of-flight data from at least one real time-of-flight camera of the real time-of-flight camera type; determine a difference between the real time-of-flight data and the time-of-flight camera model data; and update, based on the determined difference, the time-of-flight camera model for generating simulated time-of-flight data representing the real time-of-flight camera.

Abstract: The present disclosure proposes a concept of training a machine learning network for use with a ToF camera. Based on a predefined synthetic scene, it is simulated a ground truth time resolved illumination return signal for a light pulse emitted from the ToF camera to the synthetic scene and scattered back from the synthetic scene to the ToF camera. The synthetic scene comprises a plurality of scene points with known distances between each of the scene points and the ToF camera. Based on the ground truth time resolved illumination return signal and a simulation model of the ToF camera, it is then simulated an output signal of at least one ToF pixel capturing the synthetic scene.