Update on Overleaf.

presentation/modules/motivation.tex

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+\section{Motivation - why differentiable rendering is important}
+\begin{frame}
+    \centering
+    \Huge
+    Motivation - why differentiable rendering is important
+\end{frame}
+\begin{frame}{Importance of differentiable rendering}
+    \begin{block}{Examples for Applications}
+        \begin{itemize}
+            \item Learning-based Inverse Rendering of Complex Indoor Scenes
+with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
+            $\rightarrow$ Inverse rendering 
+            \item Generating Semantic Adversarial Examples with Differentiable Rendering [\cite{DBLP:journals/corr/abs-1910-00727}]\\
+            $\rightarrow$ Machine learning
+            \item Real-Time Lighting Estimation for Augmented Reality [\cite{IEEE:AR_lighting_estimation}]\\
+            $\rightarrow$ Realistic real time shading for AR applications
+            \item Acoustic Camera Pose Refinement [\cite{IEEE:Ac_cam_refinment}]\\
+            $\rightarrow$ Optimize six degrees of freedom for acoustic underwater cameras
+        \end{itemize}
+    \end{block}
+\end{frame}
+\subsection{Inverse rendering}
+\begin{frame}{Inverse rendering}
+    \begin{itemize}
+        \item Conventional rendering: Synthesize an Image from a 3D scene
+        \item Inverse rendering is solving the inverse problem: Synthesize a 3D scene from images
+        \item 3D modelling can be hard and time consuming
+        \item Approach: 
+        \begin{itemize}
+            \item Approximate the 3D scene (often very coarse) 
+            \item Render the approximation differentiably
+            \item Calculate the error between the render and the images
+            \item Use ADAM or comparable gradient descent algorithm to minimize this error
+        \end{itemize}
+    \end{itemize}
+\end{frame}
+
+
+\begin{frame}{Inverse rendering - current example}
+    \centering
+  \includemedia[
+    width=0.62\linewidth,height=0.35\linewidth,
+    activate=onclick,
+    addresource=proseminar_chair.mp4,
+    playbutton=fancy,
+    transparent,
+    passcontext,
+    flashvars={
+        source=proseminar_chair.mp4
+        &autoPlay=true
+    }
+  ]{}{VPlayer.swf}
+  \\
+  
+  Source: \cite{ACM:inverse_rendering_signed_distance_function}
+\end{frame}
+\subsection{Adversarial image generation}
+\begin{frame}{Adversarial image generation}
+\begin{center}
+    \begin{minipage}{0.4\linewidth}
+        \begin{itemize}
+            \item Common Problem in machine learning: Classification\\
+            $\implies$ Given a set of labels and a set of data, assign a label to each element in the dataset
+            \item Labeled data is needed to train classifier network
+        \end{itemize}
+        \pause
+        \vspace{15mm}
+        Image source: Auth0, \href{https://auth0.com/blog/captcha-can-ruin-your-ux-here-s-how-to-use-it-right/}{CAPTCHA Can Ruin Your UX. Here’s How to Use it Right}
+    \end{minipage}
+    \begin{minipage}{0.5\linewidth}
+    \centering
+        \includegraphics[width=0.5\linewidth]{presentation/img/recaptcha_example.png}
+    \end{minipage}
+\end{center}
+\end{frame}
+\begin{frame}{Adversarial image generation}
+\begin{itemize}
+    \item Problem: Labeling training data is tedious and expensive\\
+    $\implies$ We want to automatically generate training data
+    \item One solution: Generative adversarial networks. Let two neural nets "compete"; a generator and a classifier. (e.g. AutoGAN [\cite{DBLP:AutoGAN}])\\
+    $\implies$ Impossible to make semantic changes to the image (e.g. lighting) since no knowlege of the 3D scene exists
+    \item Different solution: Generate image using differentiable raytracing, use gradient descent to optimize the result image to fall into a specific class\\
+    $\implies$ Scene parameters can be manipulated
+\end{itemize}
+\end{frame}
+
+\begin{frame}{Adversarial image generation - example [\cite{DBLP:journals/corr/abs-1910-00727}]}
+\begin{center}
+\begin{figure}
+    \begin{minipage}{0.45\linewidth}
+        \includegraphics[width=\linewidth]{presentation/img/adversarial_rendering_results/correct_car.png}
+        \includegraphics[width=\linewidth]{presentation/img/adversarial_rendering_results/correct_pedestrian.png}
+    \end{minipage}
+    \begin{minipage}{0.45\linewidth}
+        \includegraphics[width=\linewidth]{presentation/img/adversarial_rendering_results/incorrect_car.png}
+        \includegraphics[width=\linewidth]{presentation/img/adversarial_rendering_results/incorrect_pedestrian.png}
+    \end{minipage}
+    \centering
+    \caption{Left: Original images, features are correctly identified.\\
+    Right: adversarial examples, silver car is not recognized and pedestrians are identified where there are none. Only semantic features (color, position, rotation) have been changed.}
+    \label{fig:adv_img_example}
+\end{figure}
+\end{center}
+\end{frame}