  Modeling cameras for the simulation of autonomous robotics is critical for
generating synthetic images with appropriate realism to effectively evaluate a
perception algorithm in simulation. In many cases though, simulated images are
produced by traditional rendering techniques that exclude or superficially
handle processing steps and aspects encountered in the actual camera pipeline.
The purpose of this contribution is to quantify the degree to which the
exclusion from the camera model of various image generation steps or aspects
affect the sim-to-real gap in robotics. We investigate what happens if one
ignores aspects tied to processes from within the physical camera, e.g., lens
distortion, noise, and signal processing; scene effects, e.g., lighting and
reflection; and rendering quality. The results of the study demonstrate,
quantitatively, that large-scale changes to color, scene, and location have far
greater impact than model aspects concerned with local, feature-level
artifacts. Moreover, we show that these scene-level aspects can stem from lens
distortion and signal processing, particularly when considering white-balance
and auto-exposure modeling.
