@@ -112,12 +112,12 @@ This BSDF should give you a linear blend between a diffuse and a Torrance-Sparro
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Add an additional Halton-based sample generator named \texttt{halton} to your solution. The sampler should be able to produce 2D and 1D sequences, based on Halton low-discrepancy sequences that use the radical inverse. For 2D samples, use a combined base-11,13 Halton sequence and for 1D, use a base-7 Halton sequence. To minimize repeating patterns, you should initialize your Halton sampler states (use three separate state variables) to random values (\texttt{rand()}). Try your implementation on the light surface sampling scene. Usually, such simple samplers should only be used for individual effects (e.g., picking subpixel coordinates for rays), not the full rendering procedure, but direct lighting is simple enough, so it actually works out ok. If you want to break it, you can change 2D sampling to use base-2,3 instead and see what happens!
Add an additional Halton-based sample generator named \texttt{halton} to your solution. The sampler should be able to produce 2D and 1D sequences, based on Halton low-discrepancy sequences that use the radical inverse. For 2D samples, use a combined base-11,13 Halton sequence and for 1D, use a base-7 Halton sequence. To minimize repeating patterns, you should initialize your Halton sampler states (use three separate state variables) to random values (\texttt{rand()}). Try your implementation on the light surface sampling scene. Usually, such simple samplers should only be used for individual effects (e.g., picking subpixel coordinates for rays), not the full rendering procedure, but direct lighting is simple enough, so it actually works out ok. If you want to break it, you can try it on full path tracing scenes or change 2D sampling to use base-2,3 instead and see what happens!
For 10 bonus points, implement a sophisticated Halton-based sampling strategy that actually can replace the independent sampler completely! Hints and suggestions for making it work are described in the \href{https://www.pbr-book.org/3ed-2018/Sampling_and_Reconstruction/The_Halton_Sampler}{course book}. Make sure that your renderings with Halton converge to the same result as with the independent sampler!
\subsection{Support for Filtering (1 Point)}
When you fixed aliasing and computed by integrating values over the whole pixel, you basically used a pixel-sized box filter.
When you fixed aliasing and computed output colors by integrating values over the whole pixel, you basically used a pixel-sized box filter.
This is easy to implement, but really not a good choice for filtering: the box filter is sometimes jokingly referred to as the worst filter available.
To get support for a few different filters, you need to implement the corresponding suppport in Nori.
Once done, you should experiment with different filters and sample counts, to see what a difference they can make.
