  The use of simulated virtual environments to train deep convolutional neural
networks (CNN) is a currently active practice to reduce the
(real)data-hungriness of the deep CNN models, especially in application domains
in which large scale real data and/or groundtruth acquisition is difficult or
laborious. Recent approaches have attempted to harness the capabilities of
existing video games, animated movies to provide training data with high
precision groundtruth. However, a stumbling block is in how one can certify
generalization of the learned models and their usefulness in real world data
sets. This opens up fundamental questions such as: What is the role of
photorealism of graphics simulations in training CNN models? Are the trained
models valid in reality? What are possible ways to reduce the performance bias?
In this work, we begin to address theses issues systematically in the context
of urban semantic understanding with CNNs. Towards this end, we (a) propose a
simple probabilistic urban scene model, (b) develop a parametric rendering tool
to synthesize the data with groundtruth, followed by (c) a systematic
exploration of the impact of level-of-realism on the generality of the trained
CNN model to real world; and domain adaptation concepts to minimize the
performance bias.
