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Update on Overleaf.

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uxwmp 2023-06-18 12:30:35 +00:00 • committed by node
parent 48998b7380
commit 257108290e
4 changed files with 41 additions and 19 deletions
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8
presentation/modules/basic_terms.tex
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@ -33,9 +33,9 @@
\underbrace{\epsilon(x,x')}_{\text{emissive light}}+\underbrace{\int_S \rho(x,x',x'')I(x',x'')dx''}_{\text{light scattered towards the point}}
\right]
\]
\begin{itemize}
\item Attempts to capture the physical light transport phenomenon in a single equation
\end{itemize}
%\begin{itemize}
%\item Attempts to capture the physical light transport phenomenon in a single equation
%\end{itemize}
[\cite{ACM:rendering_equation}]
\item Problem: This equation is not analytically solvable\\
$\rightarrow$ Solve numerically using Monte-Carlo integration (i.e. raytracing)
@ -51,7 +51,7 @@
\item Calculate geometry intersection
\item Trace rays from intersection point to all light sources
\item Calculate color from emission and the sampled reflected light taking geometry into account (e.g. occlusion)
\item Have the ray "bounce around" to account for global illumination
\item Have the ray "bounce around" to account for indirect lighting
\end{itemize}
\end{block}
\pause

20
presentation/modules/motivation.tex
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@ -23,14 +23,14 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM: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 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 Approximate the 3D scene
\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
\item Calculate the error
\item Use a gradient descent algorithm to minimize this error
\end{itemize}
\end{itemize}
\end{frame}
@ -75,11 +75,11 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\end{frame}
\begin{frame}{Adversarial image generation}
\begin{itemize}
\item Problem: Labeling training data is tedious and expensive\\
\item Problem: Labeling training data is tedious\\
$\implies$ 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\\
\item One solution: Generative adversarial networks. (e.g. AutoGAN [\cite{DBLP:AutoGAN}])\\
$\implies$ Impossible to make semantic changes to the image (e.g. lighting)
\item Different solution: Use differentiable raytracing\\
$\implies$ Scene parameters can be manipulated
\end{itemize}
\end{frame}
@ -97,7 +97,7 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\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.}
Right: adversarial examples, missing/wrong identifications after only semantic changes}
\label{fig:adv_img_example}
\end{figure}
\end{center}

12
presentation/modules/problems.tex
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@ -7,9 +7,11 @@
\subsection{Why differentiable rendering is hard}
\begin{frame}{Why differentiable rendering is hard}
\begin{itemize}
\item Rendering integral contains the geometry term that is not differentiable
\item The gradiant of the visibility can lead to dirac delta terms which have 0 probability of being sampled correctly [\cite{ACM:diracdelta},\cite{ACM:diffable_raytracing}]
\item Differentiation with respect to certain scene parameters possible but we need to differentiate with respect to any scene parameter
\item Geometry term
\item Causes dirac delta terms\\
$\implies$ Have 0 probability of being sampled correctly [\cite{ACM:diracdelta},\cite{ACM:diffable_raytracing}]
%\item Differentiation with respect to certain scene parameters possible but we need to differentiate with respect to any scene parameter
\item Need to differentiate with respect to any scene parameter
\end{itemize}
\end{frame}
\begin{frame}{primary occlusion}
@ -26,8 +28,8 @@
\begin{itemize}
\item OpenDR [\cite{DBLP:OpenDR}]
\item Neural 3D Mesh Renderer [\cite{DBLP:Neural3DKatoetal}]
\item Both rasterization based (first render the image using rasterization, then approximate the gradients using the resulting color buffer)
\item Focused on speed rather than precision
\item Both rasterization based %(first render the image using rasterization, then approximate the gradients using the resulting color buffer)
\item Focused on speed $\rightarrow$ impercise
\end{itemize}
\end{block}
\end{frame}

20
presentation/modules/this_method.tex
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@ -6,8 +6,28 @@
\end{frame}
\subsection{Edge sampling}
\begin{frame}{Edge sampling}
\begin{block}{Assumptions}
\begin{itemize}
\item Continuous parameter set
\item Triangle meshes
\item No interpenetrating triangles
\item No point lights, no perfectly specular surfaces
\item Ignore time domain
\end{itemize}
\end{block}
\pause
\begin{block}{Idea}
\begin{itemize}
\item Traditional sampling for continuous regions
\item Edge sampling the discontinuous part
\end{itemize}
\end{block}
\end{frame}
\begin{frame}{Edge sampling - Illustration}
\end{frame}
\subsection{conclusion - what can this method do?}
% talk about limitations here!