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\begin{document}
\author{Jim Martens}
\title{UE4Sim}
\date{\today}

\begin{frame}
    \titlepage
\end{frame}

\begin{frame}{Paper}
    \centering
    M. Mueller, V. Casser, J. Lahoud, N. Smith und B. Ghanem,
    „UE4Sim: A Photo-Realistic Simulator for Computer Vision Applications“,
    2017. arXiv: 1708.05869. Adresse: https://arxiv.org/abs/1708.05869
\end{frame}

\begin{frame}{Problem}
    \begin{itemize}
        \item enough realistic training data is hard to come by
        \vfill
        \item human labelling is expensive
        \vfill
        \item data about some situations cannot be collected in real world
              in a controllable manner
    \end{itemize}
\end{frame}

\begin{frame}{Contribution}
    \begin{enumerate}
        \item end-to-end physics-based, fully customizable, open-source simulator
              environment for CV community working on autonomous navigation,
              tracking and a wide variety of other applications
        \vfill
        \item customizable synthetic world generation system
        \vfill
        \item novel approach for tracker evaluation with a high-fidelity real-time
              visual tracking simulator
        \vfill
        \item novel, robust deep learning based approach for autonomous driving
              that is flexible and doesn't require manually collected data
    \end{enumerate}
\end{frame}

\begin{frame}{Method \& Results}
    Autonomous driving:
    \begin{itemize}
        \item deep neural network only for pathway estimation
        \item generalizes well and performs better than humans
    \end{itemize}
\end{frame}

\begin{frame}{Usage}
    \begin{itemize}
        \item the simulator can be used to generate realistic scenarios
        \vfill
        \item DNN approach for autonomous driving could be adapted for robots
        \vfill
        \item generalizability could be far better than existing networks trained
              with existing dataset
    \end{itemize}
\end{frame}

\end{document}