104 lines
No EOL
4.2 KiB
TeX
104 lines
No EOL
4.2 KiB
TeX
\section{Motivation~-~why differentiable rendering is important}
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\begin{frame}
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\centering
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\Huge
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Motivation~-~why differentiable rendering is important
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\end{frame}
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\begin{frame}{Importance of differentiable rendering}
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\begin{block}{Examples for Applications}
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\begin{itemize}
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\item Learning-based Inverse Rendering of Complex Indoor Scenes
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with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
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$\rightarrow$ Inverse rendering
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\item Generating Semantic Adversarial Examples with Differentiable Rendering [\cite{DBLP:journals/corr/abs-1910-00727}]\\
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$\rightarrow$ Machine learning
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\item Real-Time Lighting Estimation for Augmented Reality [\cite{IEEE:AR_lighting_estimation}]\\
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$\rightarrow$ Realistic real time shading for AR applications
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\item Acoustic Camera Pose Refinement [\cite{IEEE:Ac_cam_refinment}]\\
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$\rightarrow$ Optimize six degrees of freedom for acoustic underwater cameras
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\end{itemize}
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\end{block}
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\end{frame}
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\subsection{Inverse rendering}
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\begin{frame}{Inverse rendering}
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\begin{itemize}
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\item Conventional rendering: Synthesize an Image from a 3D scene
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\item Inverse problem: Synthesize a 3D scene from images
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%\item 3D modelling can be hard and time consuming
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\item Approach:
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\begin{itemize}
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\item Approximate the 3D scene
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\item Render the approximation differentiably
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\item Calculate the error
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\item Use a gradient descent algorithm to minimize this error
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\end{itemize}
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\end{itemize}
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\end{frame}
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\begin{frame}{Inverse rendering~-~current example}
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\centering
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\includemedia[
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width=0.62\linewidth,height=0.35\linewidth,
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activate=onclick,
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addresource=proseminar_chair.mp4,
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playbutton=fancy,
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transparent,
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passcontext,
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flashvars={
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source=proseminar_chair.mp4
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&autoPlay=true
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}
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]{}{VPlayer.swf}
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\\
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Source:~\cite{ACM:inverse_rendering_signed_distance_function}
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\end{frame}
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\subsection{Adversarial image generation}
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\begin{frame}{Adversarial image generation}
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\begin{center}
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\begin{minipage}{0.4\linewidth}
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\begin{itemize}
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\item Common Problem in machine learning: Classification\\
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$\implies$ Given a set of labels and a set of data, assign a label to each element in the dataset
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\item Labeled data is needed to train classifier network
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\end{itemize}
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\pause{}
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\vspace{15mm}
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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}
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\end{minipage}
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\begin{minipage}{0.5\linewidth}
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\centering
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\includegraphics[width=0.5\linewidth]{img/recaptcha_example.png}
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\end{minipage}
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\end{center}
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\end{frame}
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\begin{frame}{Adversarial image generation}
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\begin{itemize}
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\item Problem: Labeling training data is tedious\\
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$\implies$ We want to automatically generate training data
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\item One solution: Generative adversarial networks (e.g. AutoGAN [\cite{DBLP:AutoGAN}]).\\
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$\implies$ Impossible to make semantic changes to the image
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\item Different solution: Use differentiable raytracing\\
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$\implies$ Scene parameters can be manipulated
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\end{itemize}
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\end{frame}
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\begin{frame}{Adversarial image generation~-~example [\cite{DBLP:journals/corr/abs-1910-00727}]}
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\begin{center}
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\begin{figure}
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\begin{minipage}{0.45\linewidth}
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\includegraphics[width=\linewidth]{img/adversarial_rendering_results/correct_car.png}
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\includegraphics[width=\linewidth]{img/adversarial_rendering_results/correct_pedestrian.png}
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\end{minipage}
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\begin{minipage}{0.45\linewidth}
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\includegraphics[width=\linewidth]{img/adversarial_rendering_results/incorrect_car.png}
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\includegraphics[width=\linewidth]{img/adversarial_rendering_results/incorrect_pedestrian.png}
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\end{minipage}
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\centering
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\caption{Left: Original images, features are correctly identified.\\
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Right: adversarial examples, missing/wrong identifications after only semantic changes}
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\label{fig:adv_img_example}
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\end{figure}
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\end{center}
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\end{frame} |