  The focus of this contribution is on camera simulation as it comes into play
in simulating autonomous robots for their virtual prototyping. We propose a
camera model validation methodology based on the performance of a perception
algorithm and the context in which the performance is measured. This approach
is different than traditional validation of synthetic images, which is often
done at a pixel or feature level, and tends to require matching pairs of
synthetic and real images. Due to the high cost and constraints of acquiring
paired images, the proposed approach is based on datasets that are not
necessarily paired. Within a real and a simulated dataset, A and B,
respectively, we find subsets Ac and Bc of similar content and judge,
statistically, the perception algorithm's response to these similar subsets.
This validation approach obtains a statistical measure of performance
similarity, as well as a measure of similarity between the content of A and B.
The methodology is demonstrated using images generated with Chrono::Sensor and
a scaled autonomous vehicle, using an object detector as the perception
algorithm. The results demonstrate the ability to quantify (i) differences
between simulated and real data; (ii) the propensity of training methods to
mitigate the sim-to-real gap; and (iii) the context overlap between two
datasets.
