  The goal of this paper is to assess the impact of noise in 3D camera-captured
data by modeling the noise of the imaging process and applying it on synthetic
training data. We compiled a dataset of specifically constructed scenes to
obtain a noise model. We specifically model lateral noise, affecting the
position of captured points in the image plane, and axial noise, affecting the
position along the axis perpendicular to the image plane. The estimated models
can be used to emulate noise in synthetic training data. The added benefit of
adding artificial noise is evaluated in an experiment with rendered data for
object segmentation. We train a series of neural networks with varying levels
of noise in the data and measure their ability to generalize on real data. The
results show that using too little or too much noise can hurt the networks'
performance indicating that obtaining a model of noise from real scanners is
beneficial for synthetic data generation.
