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Update on Overleaf.

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uxwmp 2023-06-20 20:56:35 +00:00 • committed by node
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presentation/modules/motivation.tex
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@ -1,10 +1,10 @@
\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}
\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
@ -19,8 +19,8 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\end{itemize}
\end{block}
\end{frame}
\subsection{Inverse rendering}
\begin{frame}{Inverse rendering}
\subsection{Inverse Rendering}
\begin{frame}{Inverse Rendering}
\begin{itemize}
\item Conventional rendering: Synthesize an Image from a 3D scene
\item Inverse problem: Synthesize a 3D scene from images
@ -36,7 +36,7 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\end{frame}
\begin{frame}{Inverse rendering~-~current example}
\begin{frame}{Inverse Rendering~-~Current Example}
\centering
\includemedia[
width=0.62\linewidth,height=0.35\linewidth,
@ -54,12 +54,12 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
Source:~\cite{ACM:inverse_rendering_signed_distance_function}
\end{frame}
\subsection{Adversarial image generation}
\begin{frame}{Adversarial image generation}
\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\\
\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}
@ -73,7 +73,7 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\end{minipage}
\end{center}
\end{frame}
\begin{frame}{Adversarial image generation}
\begin{frame}{Adversarial Image Generation}
\begin{itemize}
\item Problem: Labeling training data is tedious\\
$\implies$ We want to automatically generate training data
@ -84,7 +84,7 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\end{itemize}
\end{frame}
\begin{frame}{Adversarial image generation~-~example [\cite{DBLP:journals/corr/abs-1910-00727}]}
\begin{frame}{Adversarial Image Generation~-~Example [\cite{DBLP:journals/corr/abs-1910-00727}]}
\begin{center}
\begin{figure}
\begin{minipage}{0.45\linewidth}
@ -96,8 +96,8 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\includegraphics[width=\linewidth]{img/adversarial_rendering_results/incorrect_pedestrian.png}
\end{minipage}
\centering
\caption{Left: Original images, features are correctly identified.\\
Right: adversarial examples, missing/wrong identifications after only semantic changes}
\caption{Left: Original images, features are correctly identified\\
Right: Adversarial examples, missing/wrong identifications after only semantic changes}
\label{fig:adv_img_example}
\end{figure}
\end{center}