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Modeling cameras for the simulation of autonomous robotics is critical forgenerating synthetic images with appropriate realism to effectively evaluate aperception algorithm in simulation. In many cases though, simulated images areproduced by traditional rendering techniques that exclude or superficiallyhandle processing steps and aspects encountered in the actual camera pipeline.The purpose of this contribution is to quantify the degree to which theexclusion from the camera model of various image generation steps or aspectsaffect the sim-to-real gap in robotics. We investigate what happens if oneignores aspects tied to processes from within the physical camera, e.g., lensdistortion, noise, and signal processing; scene effects, e.g., lighting andreflection; and rendering quality. The results of the study demonstrate,quantitatively, that large-scale changes to color, scene, and location have fargreater impact than model aspects concerned with local, feature-levelartifacts. Moreover, we show that these scene-level aspects can stem from lensdistortion and signal processing, particularly when considering white-balanceand auto-exposure modeling.

Camera simulation for robot simulation: how important are various camera model components?