CIS565-Fall-2021 · UserRYang · Oct 22, 2021 · Oct 23, 2021 · Oct 23, 2021 · Oct 23, 2021
diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -73,6 +73,7 @@ set(headers
     src/sceneStructs.h
     src/preview.h
     src/utilities.h
+    stream_compaction/common.h
     )
 
 set(sources
@@ -84,6 +85,7 @@ set(sources
     src/scene.cpp
     src/preview.cpp
     src/utilities.cpp
+    stream_compaction/common.cu
     )
 
 set(imgui

diff --git a/README.md b/README.md
@@ -1,13 +1,240 @@
-CUDA Denoiser For CUDA Path Tracer
-==================================
+Project 4 CUDA A Trous Denoiser
+======================
 
 **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)
+* Raymond Yang
+	* [LinkedIn](https://www.linkedin.com/in/raymond-yang-b85b19168)
+	* Tested on: 
+		* 10/22/2021
+		* Windows 10
+		* NVIDIA GeForce GTX 1080 Ti. 
+	* Submitted on: 10/22/2021
 
-### (TODO: Your README)
 
-*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.
+## Introduction 
+The objective of this project was to implement A Trous denoiser detailed in this [paper](https://jo.dreggn.org/home/2010_atrous.pdf). To construct the denoised image, we modulate a noisy path traced image (represented as a color buffer) with a geometry buffer/G buffer (represented as an array of positions and an array of normals). Both elements of the G buffer are cahced on first bounce and only done once per render. 
 
+### Components of A Trous Denoiser
+Scene Colors:
+<p align="center">
+  <img src="img/a0.PNG" alt="drawing" width="400" />
+</p>
+
+Scene Positions (relative to camera origin):
+<p align="center">
+  <img src="img/a1.PNG" alt="drawing" width="400" />
+</p>
+
+Scene Normals: 
+<p align="center">
+  <img src="img/a2.PNG" alt="drawing" width="400" />
+</p>
+
+Scene Time to Intersect (unused): 
+<p align="center">
+  <img src="img/a3.PNG" alt="drawing" width="400" />
+</p>
+
+### Denoised Output
+<p align="center">
+  <img src="img/a4.PNG" alt="drawing" width="400" />
+</p>
+
+## Performance Analysis
+In this section, we compare the runtime costs of A Trous denoising, recorded in microseconds. For Simple Scene, we will use `scene/cornell_ceiling_light.txt`. For Slightly Less Simple Scene, we will use `scene/demo.txt`. For the purposes of consistency between runs, the following values will be hardcoded to the window unless otherwise specified: 
+* `Filter Size = 80`
+* `Color Weight = 8.f`
+* `Normal Weight = 1.f`
+* `Position Weight = 1.5f`
+
+### Simple Scene
+Baseline image for reference at 1000 iterations: 
+<p align="center">
+  <img src="img/b0.PNG" alt="drawing" width="800" />
+</p>
+Path trace time for 1000 iterations: 33679.50 ms
+Denoise time for 1000 iterations: 62.97 ms
+
+Path traced image after 10 iterations:
+<p align="center">
+  <img src="img/b1.PNG" alt="drawing" width="800" />
+</p>
+
+Path traced image after 10 iterations with A Trous denoiser:
+<p align="center">
+  <img src="img/b2.PNG" alt="drawing" width="800" />
+</p>
+
+#### Cost of Running Denoiser vs Path Tracer
+| Trial           | 1        | 2        | 3        | 4        | 5        |
+|-----------------|----------|----------|----------|----------|----------|
+| Path Trace Time | 326.05   | 338.88   | 336.49   | 345.53   | 338.01   |
+| Denoise Time    | 62.95    | 64.02    | 62.62    | 64.47    | 64.56    |
+
+Lower is better:
+<p align="center">
+  <img src="img/c0.PNG" alt="drawing" width="400" />
+</p>
+
+#### Cost of Running Denoiser vs Resolution
+| Resolution | 256x256   | 512x512   | 1024x1024 |
+|------------|-----------|-----------|-----------|
+| Time (ms)  | 4.76      | 20.41     | 116.61    |
+
+Lower is better:
+<p align="center">
+  <img src="img/c1.PNG" alt="drawing" width="400" />
+</p>
+
+Lower is better:
+<p align="center">
+  <img src="img/c3.PNG" alt="drawing" width="400" />
+</p>
+* The increase in time is proportional to the increase in pixelcount for smaller resolutions. 
+
+#### Cost of Running Denoiser vs Filter Size
+| Filter Size | 25    | 50    | 100   |
+|-------------|-------|-------|-------|
+| Time (ms)   | 45.66 | 60.41 | 65.53 |
+
+Lower is better:
+<p align="center">
+  <img src="img/c2.PNG" alt="drawing" width="400" />
+</p>
+
+* Note that the increase in time is not substanctial indicating that I am likely calculating the number of iterations required for A Trous denoise incorrectly. 
+
+### Slightly Less Simple Scene
+Baseline image for reference at 1000 iterations: 
+<p align="center">
+  <img src="img/d0.PNG" alt="drawing" width="800" />
+</p>
+Path trace time for 1000 iterations: 237650.00 ms
+Denoise time for 1000 iterations: 86.21 ms
+
+Path traced image after 10 iterations:
+<p align="center">
+  <img src="img/d1.PNG" alt="drawing" width="800" />
+</p>
+
+Path traced image after 10 iterations with A Trous denoiser:
+<p align="center">
+  <img src="img/d2.PNG" alt="drawing" width="800" />
+</p>
+
+#### Cost of Running Denoiser vs Path Tracer
+| Trial           | 1         | 2         | 3         | 4         | 5         |
+|-----------------|-----------|-----------|-----------|-----------|-----------|
+| Path Trace Time | 2431.57   | 2365.28   | 2374.86   | 2480.54   | 2387.57   |
+| Denoise Time    | 86.39     | 87.75     | 87.64     | 87.86     | 85.97     |
+
+Lower is better:
+<p align="center">
+  <img src="img/e0.PNG" alt="drawing" width="400" />
+</p>
+
+#### Cost of Running Denoiser vs Resolution
+| Resolution | 256x256   | 512x512   | 1024x1024 |
+|------------|-----------|-----------|-----------|
+| Time (ms)  | 6.61      | 30.41     | 134.44    |
+
+Lower is better:
+<p align="center">
+  <img src="img/e1.PNG" alt="drawing" width="400" />
+</p>
+
+Lower is better:
+<p align="center">
+  <img src="img/e2.PNG" alt="drawing" width="400" />
+</p>
+* The increase in time is proportional to the increase in pixelcount for tested resolutions. 
+
+#### Cost of Running Denoiser vs Filter Size
+| Filter Size | 25    | 50    | 100   |
+|-------------|-------|-------|-------|
+| Time (ms)   | 64.50 | 82.70 | 91.55 |
+
+Lower is better:
+<p align="center">
+  <img src="img/e3.PNG" alt="drawing" width="400" />
+</p>
+
+* Note that the increase in time is not substanctial indicating that I am likely calculating the number of iterations required for A Trous denoise incorrectly. 
+
+## Visual Analysis
+In this section, we compare visual effects of A Trous denoising. 
+
+### Visual Results vs Filter Size
+Filter size is the two dimensional area around a pixel. That is, for a filter size of 25 and a pixel located at `(i,j)` of the image, the surrounding `5x5` grid centered around the pixel are factored into the weighting A Trous denoiser. 
+* Since I am using a gaussian weighting spread of `5x5`, filter sizes below 25 are not considered because the area is smaller than the gaussian spread. 
+* Filter sizes in `[25, 81)`, are mapped `1:1` to the gaussian spread. Filter sizes in `[81, 100]` are mapped 2:1 to the gaussian spread. That is, the gaussian weight at `(-2, -2)` relative to the pixel will actually be the pixel located at `(-4, -4)` relative to the pixel. Likewise, `(1, 1)` is mapped to `(2, 2)`. 
+* NOTE: that this assumes I understand and implemented filter size correction. Given the observations above (runtime for a filter size of 50 is greater than runtime for a filter size of 25), it is very likely I have not implemented filter size correctly.
+
+`filterSize = 25`:
+<p align="center">
+  <img src="img/f0.PNG" alt="drawing" width="800" />
+</p>
+
+`filterSize = 50`:
+<p align="center">
+  <img src="img/f1.PNG" alt="drawing" width="800" />
+</p>
+
+`filterSize = 75`:
+<p align="center">
+  <img src="img/f2.PNG" alt="drawing" width="800" />
+</p>
+
+`filterSize = 100`:
+<p align="center">
+  <img src="img/f3.PNG" alt="drawing" width="800" />
+</p>
+
+The only substantive change seems to be between `filterSize = 25` and `filterSize = 50`.
+
+### Visual Results vs Material
+From left to right, the spheres are of properties reflective, refractive (with index of 1.5), and diffuse. Scene can be found in file `/scenes/cornell_ceiling_light_plus.txt`.
+
+1000 iterations:
+<p align="center">
+  <img src="img/g0.PNG" alt="drawing" width="800" />
+</p>
+
+10 iterations:
+<p align="center">
+  <img src="img/g1.PNG" alt="drawing" width="800" />
+</p>
+
+10 iterations with A Trous diffuse:
+<p align="center">
+  <img src="img/g2.PNG" alt="drawing" width="800" />
+</p>
+
+With my implementation, A Trous denoiser fails to adequately simulate refractive properties. It is moderately capable of simulating diffuse properties. It is capable of simulating reflective properties. 
+
+### Visual Results vs Lighting
+Small light uses scene: `/scenes/cornell.txt`
+Large light uses scene: `/scenes/cornell_ceiling_light.txt`
+
+Cornell Box with small ceiling light: 
+<p align="center">
+  <img src="img/h0.PNG" alt="drawing" width="800" />
+</p>
+
+Cornell Box with large ceiling light:
+<p align="center">
+  <img src="img/h1.PNG" alt="drawing" width="800" />
+</p>
+
+Cornell Box with small ceiling light and A Trous denoise:
+<p align="center">
+  <img src="img/h2.PNG" alt="drawing" width="800" />
+</p>
+
+Cornell Box with large ceiling light and A Trous denoise:
+<p align="center">
+  <img src="img/h3.PNG" alt="drawing" width="800" />
+</p>
+
+Given poorer lighting conditions, more "holes" are left in the image. As the A Trous denoiser interpolates neighboring colors for each pixel, it emphasizes darker colors leading to poorer results. 
diff --git a/img/a0.PNG b/img/a0.PNG
diff --git a/img/a1.PNG b/img/a1.PNG
diff --git a/img/a2.PNG b/img/a2.PNG
diff --git a/img/a3.PNG b/img/a3.PNG
diff --git a/img/a4.PNG b/img/a4.PNG
diff --git a/img/b0.PNG b/img/b0.PNG
diff --git a/img/b1.PNG b/img/b1.PNG
diff --git a/img/b2.PNG b/img/b2.PNG
diff --git a/img/c0.PNG b/img/c0.PNG
diff --git a/img/c1.PNG b/img/c1.PNG
diff --git a/img/c2.PNG b/img/c2.PNG
diff --git a/img/c3.PNG b/img/c3.PNG
diff --git a/img/d0.PNG b/img/d0.PNG
diff --git a/img/d1.PNG b/img/d1.PNG
diff --git a/img/d2.PNG b/img/d2.PNG
diff --git a/img/e0.PNG b/img/e0.PNG
diff --git a/img/e1.PNG b/img/e1.PNG
diff --git a/img/e2.PNG b/img/e2.PNG
diff --git a/img/e3.PNG b/img/e3.PNG
diff --git a/img/f0.PNG b/img/f0.PNG
diff --git a/img/f1.PNG b/img/f1.PNG
diff --git a/img/f2.PNG b/img/f2.PNG
diff --git a/img/f3.PNG b/img/f3.PNG
diff --git a/img/g0.PNG b/img/g0.PNG
diff --git a/img/g1.PNG b/img/g1.PNG
diff --git a/img/g2.PNG b/img/g2.PNG
diff --git a/img/h0.PNG b/img/h0.PNG
diff --git a/img/h1.PNG b/img/h1.PNG
diff --git a/img/h2.PNG b/img/h2.PNG
diff --git a/img/h3.PNG b/img/h3.PNG
diff --git a/scenes/cornell.txt b/scenes/cornell.txt
@@ -52,7 +52,7 @@ EMITTANCE   0
 CAMERA
 RES         800 800
 FOVY        45
-ITERATIONS  5000
+ITERATIONS  10
 DEPTH       8
 FILE        cornell
 EYE         0.0 5 10.5