CIS565-Fall-2013 · vivreddy · Sep 29, 2013 · Sep 30, 2013 · Sep 30, 2013 · Oct 1, 2013
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,12 @@
+PROJ1_WIN/565Pathtracer/Debug/
+PROJ1_WIN/565Pathtracer/Release/
+PROJ1_WIN/Debug/
+PROJ1_WIN/Release/
+PROJ1_WIN/ipch/565pathtracer-beee273d/565pathtracer-d00238b4.ipch
+PROJ1_WIN/565Pathtracer/tmpxft_00001eac_00000000-6_raytraceKernel.cpp3.o
+*.opensdf
+*.sdf
+*.obj
+*.pdb
+*.o
+*.ipch
diff --git a/PROJ1_WIN/565Pathtracer.suo b/PROJ1_WIN/565Pathtracer.suo
diff --git a/PROJ1_WIN/565Pathtracer/565Pathtracer.vcxproj.user b/PROJ1_WIN/565Pathtracer/565Pathtracer.vcxproj.user
@@ -16,4 +16,12 @@
     <LocalDebuggerCommandArguments>scene=../../scenes/sampleScene.txt</LocalDebuggerCommandArguments>
     <DebuggerFlavor>WindowsLocalDebugger</DebuggerFlavor>
   </PropertyGroup>
-</Project>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
+    <LocalDebuggerCommandArguments>scene=../../scenes/sampleScene.txt</LocalDebuggerCommandArguments>
+    <DebuggerFlavor>WindowsLocalDebugger</DebuggerFlavor>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
+    <LocalDebuggerCommandArguments>scene=../../scenes/sampleScene.txt</LocalDebuggerCommandArguments>
+    <DebuggerFlavor>WindowsLocalDebugger</DebuggerFlavor>
+  </PropertyGroup>
+</Project>
diff --git a/PROJ1_WIN/565Pathtracer/test.0 - Copy.bmp b/PROJ1_WIN/565Pathtracer/test.0 - Copy.bmp
diff --git a/PROJ1_WIN/565Pathtracer/test.0.bmp b/PROJ1_WIN/565Pathtracer/test.0.bmp
diff --git a/PROJ1_WIN/565Pathtracer/test.1.bmp b/PROJ1_WIN/565Pathtracer/test.1.bmp
diff --git a/README.md b/README.md
@@ -6,142 +6,96 @@ Fall 2013
 Due Wednesday, 10/02/13
 -------------------------------------------------------------------------------
 
--------------------------------------------------------------------------------
-NOTE:
--------------------------------------------------------------------------------
-This project requires an NVIDIA graphics card with CUDA capability! Any card after the Geforce 8xxx series will work. If you do not have an NVIDIA graphics card in the machine you are working on, feel free to use any machine in the SIG Lab or in Moore100 labs. All machines in the SIG Lab and Moore100 are equipped with CUDA capable NVIDIA graphics cards. If this too proves to be a problem, please contact Patrick or Liam as soon as possible.
 
 -------------------------------------------------------------------------------
-INTRODUCTION:
+DESCRIPTION :
 -------------------------------------------------------------------------------
-In this project, you will extend your raytracer from Project 1 into a full CUDA based global illumination pathtracer. 
+In this Project a Path tracer is created by using the concept of GLobal Illumination. In thsi technique a bunch
+of rays are shooted into the scene and each ray is tracked until they hit the light, the color at every bounce 
+is calculated and multiplied with the previous color. This process is repeated for every iteration ,and the color 
+obtained at each iteration is averaged and added to the previous value. 
 
-For this project, you may either choose to continue working off of your codebase from Project 1, or you may choose to use the included basecode in this repository. The basecode for Project 2 is the same as the basecode for Project 1, but with some missing components you will need filled in, such as the intersection testing and camera raycasting methods. 
+All the basic requirements such as 
+* Full global illumination 
+* Properly accumulating emittance and colors to generate a final image
+* Supersampled antialiasing
+Have been implemented 
 
-How you choose to extend your raytracer into a pathtracer is a fairly open-ended problem; the supplied basecode is meant to serve as one possible set of guidelines for doing so, but you may choose any approach you want in your actual implementation, including completely scrapping the provided basecode in favor of your own from-scratch solution.
+* Parallelization by ray instead of by pixel via stream compaction 
+This was done using Thrust library and the function if_remove was used to compress the ray pool by removing 
+the dead rays.
 
--------------------------------------------------------------------------------
-CONTENTS:
--------------------------------------------------------------------------------
-The Project2 root directory contains the following subdirectories:
-
-* src/ contains the source code for the project. Both the Windows Visual Studio solution and the OSX makefile reference this folder for all source; the base source code compiles on OSX and Windows without modification.
-* scenes/ contains an example scene description file.
-* renders/ contains two example renders: the raytraced render from Project 1 (GI_no.bmp), and the same scene rendered with global illumination (GI_yes.bmp). 
-* PROJ1_WIN/ contains a Windows Visual Studio 2010 project and all dependencies needed for building and running on Windows 7.
-* PROJ1_OSX/ contains a OSX makefile, run script, and all dependencies needed for building and running on Mac OSX 10.8. 
-* PROJ1_NIX/ contains a Linux makefile for building and running on Ubuntu 
-  12.04 LTS. Note that you will need to set the following environment
-  variables: 
-
-  - PATH=$PATH:/usr/local/cuda-5.5/bin
-  - LD_LIBRARY_PATH=/usr/local/cuda-5.5/lib64:/lib
-
-The projects build and run exactly the same way as in Project0 and Project1.
+* Perfect specular reflection
+Was achieved by reflecting the rays when they hit the reflective surface 
 
--------------------------------------------------------------------------------
-REQUIREMENTS:
--------------------------------------------------------------------------------
-In this project, you are given code for:
 
-* All of the basecode from Project 1, plus:
-* Intersection testing code for spheres and cubes
-* Code for raycasting from the camera
+The extra features that were implemented are : 
 
-You will need to implement the following features. A number of these required features you may have already implemented in Project 1. If you have, you are ahead of the curve and have less work to do! 
+* Translational motion blur
+  Here the object position was varied according to linear interpolated values of its start and end 
+  position during each iteration
 
-* Full global illumination (including soft shadows, color bleeding, etc.) by pathtracing rays through the scene. 
-* Properly accumulating emittance and colors to generate a final image
-* Supersampled antialiasing
-* Parallelization by ray instead of by pixel via stream compaction
-* Perfect specular reflection
+* Snell-based Refraction, i.e. glass
+  Here the basic Snells refraction was implemented using the Glm::refract function and a probability 
+  was assigned to get both reflections and refractions on the material 
 
-You are also required to implement at least two of the following features. Some of these features you may have already implemented in Project 1. If you have, you may NOT resubmit those features and instead must pick two new ones to implement.
+* OBJ Mesh loading and rendering with Bounding boxes
+  The Obj loading was implemented earlier for ray tracer , but in this assignmnet the maximum and minimum
+  co-ordinates of the mesh were calculated and sent to the GPU and hence a max min box intersection test 
+  was done
 
-* Additional BRDF models, such as Cook-Torrance, Ward, etc. Each BRDF model may count as a separate feature. 
-* Texture mapping 
-* Bump mapping
-* Translational motion blur
-* Fresnel-based Refraction, i.e. glass
-* OBJ Mesh loading and rendering without KD-Tree
 * Interactive camera
-* Integrate an existing stackless KD-Tree library, such as CUKD (https://github.com/unvirtual/cukd)
-* Depth of field
-
-Alternatively, implementing just one of the following features can satisfy the "pick two" feature requirement, since these are correspondingly more difficult problems:
-
-* Physically based subsurface scattering and transmission
-* Implement and integrate your own stackless KD-Tree from scratch. 
-* Displacement mapping
-* Deformational motion blur
+  A interactive mouse has been implemented by imagining a camera attched to the sphere and hence the new 
+  positions are calculated based on that. To do this feature CIS 563 cloth simulation assignmnet was referred,
+  and it partially acted as a guide for path tracer interaction feature. 
 
-As yet another alternative, if you have a feature or features you really want to implement that are not on this list, let us know, and we'll probably say yes!
+* Depth of field
+  DOF was implemneted by selecting a depth plane away from the camera and jittering the camera position during 
+  each iteration.  
 
 -------------------------------------------------------------------------------
-NOTES ON GLM:
+Working Video :
 -------------------------------------------------------------------------------
-This project uses GLM, the GL Math library, for linear algebra. You need to know two important points on how GLM is used in this project:
-
-* In this project, indices in GLM vectors (such as vec3, vec4), are accessed via swizzling. So, instead of v[0], v.x is used, and instead of v[1], v.y is used, and so on and so forth.
-* GLM Matrix operations work fine on NVIDIA Fermi cards and later, but pre-Fermi cards do not play nice with GLM matrices. As such, in this project, GLM matrices are replaced with a custom matrix struct, called a cudaMat4, found in cudaMat4.h. A custom function for multiplying glm::vec4s and cudaMat4s is provided as multiplyMV() in intersections.h.
-
+[Path Tracer Video](http://www.youtube.com/watch?v=bA-7rGa7juM&feature=youtu.be)
 -------------------------------------------------------------------------------
-README
+ScreenShots :
 -------------------------------------------------------------------------------
-All students must replace or augment the contents of this Readme.md in a clear 
-manner with the following:
+Here is the first image with the basic diffuse applied on all the materials 
 
-* A brief description of the project and the specific features you implemented.
-* At least one screenshot of your project running.
-* A 30 second or longer video of your project running.  To create the video you
-  can use http://www.microsoft.com/expression/products/Encoder4_Overview.aspx 
-* A performance evaluation (described in detail below).
+![alt tag](https://raw.github.com/vivreddy/Project2-Pathtracer/master/renders/3.png)
 
--------------------------------------------------------------------------------
-PERFORMANCE EVALUATION
--------------------------------------------------------------------------------
-The performance evaluation is where you will investigate how to make your CUDA
-programs more efficient using the skills you've learned in class. You must have
-performed at least one experiment on your code to investigate the positive or
-negative effects on performance. 
 
-One such experiment would be to investigate the performance increase involved 
-with adding a spatial data-structure to your scene data.
+With Reflections applied 
+![alt tag](https://raw.github.com/vivreddy/Project2-Pathtracer/master/renders/4.png)
 
-Another idea could be looking at the change in timing between various block
-sizes.
+With refractions applied 
+![alt tag](https://raw.github.com/vivreddy/Project2-Pathtracer/master/renders/5.png)
 
-A good metric to track would be number of rays per second, or frames per 
-second, or number of objects displayable at 60fps.
+With Depth of Field 
+![alt tag](https://raw.github.com/vivreddy/Project2-Pathtracer/master/renders/6.png)
 
-We encourage you to get creative with your tweaks. Consider places in your code
-that could be considered bottlenecks and try to improve them. 
+With Motion Blur 
+![alt tag](https://raw.github.com/vivreddy/Project2-Pathtracer/master/renders/8.png)
 
-Each student should provide no more than a one page summary of their
-optimizations along with tables and or graphs to visually explain any
-performance differences.
+With OBJ loader and bounding boxes 
+![alt tag](https://raw.github.com/vivreddy/Project2-Pathtracer/master/renders/7.png)
+
+Other renders
+![alt tag](https://raw.github.com/vivreddy/Project2-Pathtracer/master/renders/2.png)
 
--------------------------------------------------------------------------------
-THIRD PARTY CODE POLICY
--------------------------------------------------------------------------------
-* Use of any third-party code must be approved by asking on the Google group.  If it is approved, all students are welcome to use it.  Generally, we approve use of third-party code that is not a core part of the project.  For example, for the ray tracer, we would approve using a third-party library for loading models, but would not approve copying and pasting a CUDA function for doing refraction.
-* Third-party code must be credited in README.md.
-* Using third-party code without its approval, including using another student's code, is an academic integrity violation, and will result in you receiving an F for the semester.
 
--------------------------------------------------------------------------------
-SELF-GRADING
--------------------------------------------------------------------------------
-* On the submission date, email your grade, on a scale of 0 to 100, to Liam, [email protected], with a one paragraph explanation.  Be concise and realistic.  Recall that we reserve 30 points as a sanity check to adjust your grade.  Your actual grade will be (0.7 * your grade) + (0.3 * our grade).  We hope to only use this in extreme cases when your grade does not realistically reflect your work - it is either too high or too low.  In most cases, we plan to give you the exact grade you suggest.
-* Projects are not weighted evenly, e.g., Project 0 doesn't count as much as the path tracer.  We will determine the weighting at the end of the semester based on the size of each project.
 
 -------------------------------------------------------------------------------
-SUBMISSION
+PERFORMANCE EVALUATION
 -------------------------------------------------------------------------------
-As with the previous project, you should fork this project and work inside of your fork. Upon completion, commit your finished project back to your fork, and make a pull request to the master repository.
-You should include a README.md file in the root directory detailing the following
-
-* A brief description of the project and specific features you implemented
-* At least one screenshot of your project running, and at least one screenshot of the final rendered output of your pathtracer
-* Instructions for building and running your project if they differ from the base code
-* A link to your blog post detailing the project
-* A list of all third-party code used
+
+![alt tag](https://raw.github.com/vivreddy/Project2-Pathtracer/master/renders/table.png)
+
+In this Performance evaluation, the speeds or the time elapsed for with and without stream
+campaction is compared. Here initially when the number of bounces are less, without stream 
+campaction would work better because of thrust remove_if overhead calculations for smaller
+bounces.
+Whereas it can be easily seen how the performance improves when the number of threads are reduced 
+when we have more number of bounces and hence more number of rays that can be removed from the raypool
+
+
diff --git a/renders/2.png b/renders/2.png
diff --git a/renders/3.png b/renders/3.png
diff --git a/renders/4.png b/renders/4.png
diff --git a/renders/5.png b/renders/5.png
diff --git a/renders/6.png b/renders/6.png
diff --git a/renders/7.png b/renders/7.png
diff --git a/renders/8.png b/renders/8.png
diff --git a/renders/table.png b/renders/table.png