  Machine learning requires data, but acquiring and labeling real-world data is
challenging, expensive, and time-consuming. More importantly, it is nearly
impossible to alter real data post-acquisition (e.g., change the illumination
of a room), making it very difficult to measure how specific properties of the
data affect performance. In this paper, we present AI Playground (AIP), an
open-source, Unreal Engine-based tool for generating and labeling virtual image
data. With AIP, it is trivial to capture the same image under different
conditions (e.g., fidelity, lighting, etc.) and with different ground truths
(e.g., depth or surface normal values). AIP is easily extendable and can be
used with or without code. To validate our proposed tool, we generated eight
datasets of otherwise identical but varying lighting and fidelity conditions.
We then trained deep neural networks to predict (1) depth values, (2) surface
normals, or (3) object labels and assessed each network's intra- and
cross-dataset performance. Among other insights, we verified that sensitivity
to different settings is problem-dependent. We confirmed the findings of other
studies that segmentation models are very sensitive to fidelity, but we also
found that they are just as sensitive to lighting. In contrast, depth and
normal estimation models seem to be less sensitive to fidelity or lighting and
more sensitive to the structure of the image. Finally, we tested our trained
depth-estimation networks on two real-world datasets and obtained results
comparable to training on real data alone, confirming that our virtual
environments are realistic enough for real-world tasks.
