From 257108290ecc65124c44e7856b3337578cc29c41 Mon Sep 17 00:00:00 2001 From: uxwmp Date: Sun, 18 Jun 2023 12:30:35 +0000 Subject: [PATCH] Update on Overleaf. --- presentation/modules/basic_terms.tex | 8 ++++---- presentation/modules/motivation.tex | 20 ++++++++++---------- presentation/modules/problems.tex | 12 +++++++----- presentation/modules/this_method.tex | 20 ++++++++++++++++++++ 4 files changed, 41 insertions(+), 19 deletions(-) diff --git a/presentation/modules/basic_terms.tex b/presentation/modules/basic_terms.tex index 01e21a3..76d4515 100644 --- a/presentation/modules/basic_terms.tex +++ b/presentation/modules/basic_terms.tex @@ -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 diff --git a/presentation/modules/motivation.tex b/presentation/modules/motivation.tex index 4ce98e1..9209790 100644 --- a/presentation/modules/motivation.tex +++ b/presentation/modules/motivation.tex @@ -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} diff --git a/presentation/modules/problems.tex b/presentation/modules/problems.tex index caa6e85..99d7849 100644 --- a/presentation/modules/problems.tex +++ b/presentation/modules/problems.tex @@ -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} diff --git a/presentation/modules/this_method.tex b/presentation/modules/this_method.tex index 36bc7a1..db0c02c 100644 --- a/presentation/modules/this_method.tex +++ b/presentation/modules/this_method.tex @@ -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!