Project 4: Alex Fu

README.md

@@ -3,11 +3,160 @@ CUDA Denoiser For CUDA Path Tracer
 
 **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 4**
 
-* (TODO) YOUR NAME HERE
-* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab)
+**[Repo link](https://github.com/IwakuraRein/Nagi)**
 
-### (TODO: Your README)
+- Alex Fu
+
+  - [LinkedIn](https://www.linkedin.com/in/alex-fu-b47b67238/)
+  - [Twitter](https://twitter.com/AlexFu8304)
+  - [Personal Website](https://thecger.com/)
 
-*DO NOT* leave the README to the last minute! It is a crucial part of the
-project, and we will not be able to grade you without a good README.
+Tested on: Windows 10, i7-10750H @ 2.60GHz, 16GB, GTX 3060 6GB
 
+## Feature
+
+A real-time path tracing denoiser. Reference: [*Spatiotemporal variance-guided filtering: real-time reconstruction for path-traced global illumination*](https://dl.acm.org/doi/10.1145/3105762.3105770).
+
+<video src="https://user-images.githubusercontent.com/28486541/196747599-32b3307a-4af8-43af-bf47-4a27321f0234.mp4"></video>
+
+## G-Buffer optimization
+
+![](./img/gbuffer.png)
+
+In order to better represent the geometries of reflection, I blend the geometries according to material types:
+
+Diffuse material:
+
+* Albedo buffer: store first bounce albedo
+
+* Normal buffer: store first bounce normal
+
+* Depth buffer: store first bounce depth
+
+Glossy Material:
+
+- Albedo buffer: blend the first and second bounce albedo
+
+- Normal buffer: store first bounce normal
+
+- Depth buffer: store first bounce depth
+
+Specular Material:
+
+* Albedo buffer: blend the albedo until hits non-specular material
+
+* Normal buffer: store the first non-specular material's normal
+
+* Dpeth buffer: accumulate the depth until hits non-specular material
+
+This denoiser is based on SVGF so there is another buffer storing the square luma.
+
+The square luma is calculated by:
+
+$SqrLuma = (\{ 0.299, 0.587, 0.114 \} \cdot ( Color \div (Albedo + \epsilon ))^2$
+
+Also notice that the final albedo buffer is the blend of all spp's albedo buffer.
+
+## Denoiser
+
+![](./img/denoiser.png)
+
+The denoiser is essentially a bilateral wavelet filter. Its weight can be denoted by:
+
+$W = W_Z \cdot W_N \cdot W_L$
+
+where 
+
+$W_Z = \exp(-\frac{|Z(p) - Z(q)|}{\sigma_Z + \epsilon})$
+
+$W_N = \mathrm{max}(0, N(p) \cdot N(q))^{\sigma_N}$
+
+$W_L = \exp(-\frac{|L(p) - L(q)|}{\sigma_L \cdot Dev + \epsilon})$
+
+Where the luminance $L$ is calculated by
+
+$L = Color \div (Albedo + \epsilon )$
+
+The deviation $Dev$ is calculated by:
+
+$\mathrm{clamp}(g_{3 \times 3}(\sqrt{|SqrLuma(p)-(\{ 0.299, 0.587, 0.114 \}\cdot L(p))^2|}), \epsilon, 1)$
+
+where $g_{3 \times 3}$ is the 3X3 gaussian filter.
+
+In practice, the filter size is 5X5, $\sigma_Z$ is 1, $\sigma_N$ is 64, and $\sigma_L$ is 4. The image will be iteratively denoised 4 times, with the dilation of 1, 2, 4, and 8.
+
+To stabilize the preview, the GUI will blend the last result and the current result.
+
+## Comapre to Gaussian Filter
+
+<table>
+    <tr>
+        <th>wavelet filter</th>
+        <th>gaussian filter</th>
+    </tr>
+    <tr>
+        <th><img src="./img/wavelet.png"/></th>
+        <th><img src="./img/gaussian.png"/></th>
+    </tr>
+</table>
+
+The results of wavelet filter and gaussian filter look similar. However, the computation of wavelet filter is less.
+
+## Performance Analysis
+
+As shown in the demonstration video, it took 64 iterations for generating a plausible result for an inner scene.
+
+### Performance impacted by resolutions
+
+![](./img/resolutions.png)
+
+![](./img/resolutions2.png)
+
+### Performance impacted by luminance weight
+
+![](./img/Wl.png)
+
+By introducing the luminance weight, the filter will be aware of the edges between light and dark, thus better preserving the edges of shadows and light sources.
+
+Also, introducing the deviation term further improves the edge preservation and can decrease the blurring as the rendering result converges. With more and more samples, the $Dev$ and $W_L$ will become closer to zero.
+
+### Performance impacted by wavelet filter
+
+By adding dilations, wavelet filters can a have larger reception field than normal filters with similar computational costs.
+
+<table>
+    <tr>
+        <th>5x5 wavelet filter, 4 times, 4.96ms</th>
+        <th>9x9 normal filter, 5ms</th>
+    </tr>
+    <tr>
+        <th><img src="./img/5x5_wavelet.png"/></th>
+        <th><img src="./img/9x9_normal.png"/></th>
+    </tr>
+</table>
+
+At the same time cost, the wavelet filter generates better result.
+
+### Performance impacted by filter size
+
+<table>
+    <tr>
+        <th>9x9 wavelet filter, 4 times, 18.5ms</th>
+        <th>7x7 wavelet filter, 4 times, 10.9ms</th>
+    </tr>
+    <tr>
+        <th><img src="./img/9x9_wavelet.png"/></th>
+        <th><img src="./img/7x7_wavelet.png"/></th>
+    </tr>
+</table>
+
+<table>
+    <tr>
+        <th>5x5 wavelet filter, 4 times, 4.96ms</th>
+        <th>3x3 wavelet filter, 4 times, 2ms</th>
+    </tr>
+    <tr>
+        <th><img src="./img/5x5_wavelet.png"/></th>
+        <th><img src="./img/3x3_wavelet.png"/></th>
+    </tr>
+</table>