Integrate Area ReSTIR

It nicely suits the story which I want to tell as work in this area is still ongoing.
Additionally, this paper has been mentioned rather frequently in recent weeks, therefore it's likely a good integration.

report/main.bib

@@ -360,6 +360,17 @@ @article{restirGeneralized
   publisher={ACM New York, NY, USA}
 }
 
+@article{restirArea,
+  title         = {Area ReSTIR: Resampling for Real-Time Defocus and Antialiasing},
+  author        = {Zhang, Song and Lin, Daqi and Kettunen, Markus and Yuksel, Cem and Wyman, Chris},
+  month         = {July},
+  booktitle     = {ACM Transactions on Graphics (SIGGRAPH)},
+  year          = {2024},
+  volume        = {43},
+  number        = {4},
+  doi           = {10.1145/3658210},
+}
+
 @article{muller2021real,
 author = {M\"{u}ller, Thomas and Rousselle, Fabrice and Nov\'{a}k, Jan and Keller, Alexander},
 title = {Real-time neural radiance caching for path tracing},

report/parts/theory.tex

@@ -979,7 +979,7 @@ \section{Computer Graphics Technology}
 
 The first consumer graphics processing unit (\gls{GPU}), the GeForce 256 produced by NVIDIA \cite{evolutionOfGPU}, was developed in the 1990s and extend upon research conducted in the decades prior such as the superworkstations introduced by Silicon Graphics in the 1980s \cite{sigWorkstation}. These workstations were special-purpose computers which were optimized for computer graphics. The \gls{GPU} on the other hand is a specialized processor that is well-suited for workloads which require parallel processing and can be installed on a standard computer. While \gls{CPU} parallelization on consumer hardware is generally limited to a few cores, modern \glspl{GPU} have thousands of compute units which enable \gls{SIMD} and more commonly \fGls{SIMT}{\e{Single Instruction, Multiple Threads}, extension to \gls{SIMD} which is frequently used on modern \glspl{GPU}} processing. The difference is that \gls{SIMD} operates on a single processor, while \gls{SIMT} operates on multiple processors.
 
-Due to computational complexity, ray tracing techniques have been limited to offline rendering for a long time. Early ray tracers such as \gls{BMRT} were developed focusing on leveraging \gls{CPU} for computations. The introduction of \glspl{GPU} has lead to further research on how to optimize speed for techniques related to ray tracing. One such example of research for a well-established technique is \gls{BVH} construction on the \gls{GPU} \cite{lauterbach2009GPUbvh}. Leveraging \gls{GPU} has been a focus of research and has enabled real-time ray tracing in recent years. Notable developments include reservoir-based spatio-temporal importance resampling (ReSTIR) \cite{restir} and subsequent improvements \cite{restirAdvancements,restirGeneralized}.
+Due to computational complexity, ray tracing techniques have been limited to offline rendering for a long time. Early ray tracers such as \gls{BMRT} were developed focusing on leveraging \gls{CPU} for computations. The introduction of \glspl{GPU} has lead to further research on how to optimize speed for techniques related to ray tracing. One such example of research for a well-established technique is \gls{BVH} construction on the \gls{GPU} \cite{lauterbach2009GPUbvh}. Leveraging \gls{GPU} has been a focus of research and has enabled real-time ray tracing in recent years. Notable developments include reservoir-based spatio-temporal importance resampling (ReSTIR) \cite{restir} and subsequent improvements \cite{restirAdvancements,restirGeneralized,restirArea}.
 
 Nowadays \glspl{GPU} are prevalent in consumer hardware such as smartphones, tablets, laptops and desktops and their use case is not limited to computer graphics. \glspl{GPU} are used in a wide range of applications, such as machine learning (\gls{ML}), scientific computing and data processing. Devices, which do not have a discrete \gls{GPU}, often use an integrated \gls{GPU} which is part of the \gls{CPU}.