Time-of-flight camera characterization with functional modeling for synthetic scene generation

In this manuscript, we design, describe, and present a functional model of Time-of-Flight (ToF) cameras. The model can be used to generate randomized scenes that incorporate depth scenarios with various objects at various depths with varied orientations and illumination intensity. In addition to the potential to generate any random depth scenario, the camera, pixels, and binning are modelled incorporating radial distortion based on camera intrinsic and extrinsic. The model also includes ToF artifacts such as Signal Noise, Crosstalk and Multipath. We measured experimentally the Noise in Time-of-Flight. We experimentally fitted, and simulated with state-of-the art Simulator the Crosstalk effect, and characterized multipath according with the existing literature. Our work can be used to generate as many images as needed for neural network (NN) training and testing. The proposed approach can also be used to benchmark and evaluate both End-to-End ToF algorithms as well as specialized algorithms for denoising, unwrapping, crosstalk, and multipath correction.