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README.md

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@@ -19,26 +19,17 @@ This is due **INSTRUCTOR TODO** evening at midnight.
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**Summary:** **INSTRUCTOR TODO**
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In this project, you will use CUDA to implement a simplified standard
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In this project, you will use CUDA to implement a simplified
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rasterized graphics pipeline, similar to the OpenGL pipeline. You will
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implement vertex shading, primitive assembly, rasterization, fragment shading,
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and a framebuffer. More information about the rasterized graphics pipeline can
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be found in the class slides and in the CIS 560 lecture notes.
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The base code provided includes an OBJ loader and much of the mundane I/O and
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The base code provided includes an OBJ loader and much of the I/O and
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bookkeeping code. It also includes some functions that you may find useful,
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described below. The core rasterization pipeline is left for you to implement.
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A rasterizer is **NOT**:
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* A ray tracer. You shouldn't be doing any ray casting anywhere in the project
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(... unless you do some fancy raytraced effects in your fragment shader).
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This project will let you generate graphics WITHOUT the need for ray casting!
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* An OpenGL rendering engine. You shouldn't write any new OpenGL code - think
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of your project as a reimplementation of OpenGL's core pipeline.
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Finally, note that, while this base code is meant to serve as a strong starting
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point for a CUDA path tracer, you are not required to use it if you don't want
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You are not required to use this base code if you don't want
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to. You may also change any part of the base code as you please.
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**This is YOUR project.**
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In this project, you are given code for:
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* A library for loading/reading standard Alias/Wavefront `.obj` format mesh
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files and converting them to OpenGL style VBOs/IBOs
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files and converting them to OpenGL-style vertex and index buffers
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* A suggested order of kernels with which to implement the graphics pipeline
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* Working code for CUDA-GL interop
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* CUDA-GL interop
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You will need to implement the following features/pipeline stages:
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* Vertex shading.
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* (Vertex shader) perspective transformation.
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* Primitive assembly with support for triangle VBOs/IBOs.
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* Primitive assembly with support for triangle vertex and index buffers.
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* Rasterization: **either** a scanline or a tiled approach.
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* Fragment shading.
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* A depth buffer for storing and depth testing fragments.
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(the parenthesized numbers must add to 3.0 or more):
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* Additional pipeline stages.
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* (1.0) Tessellation shader.
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* (1.0) Geometry shader.
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* (1.0) Transform feedback.
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* (1.0) Stencil test.
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* (0.5) Back-face culling with stream compaction.
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* (0.5) Scissor test.
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* (0.5) Blending.
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* (1.0) Instancing
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* (0.5) Correct color interpolation between points on a primitive.
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* (1.0) UV texture mapping with bilinear texture filtering and perspective correct texture coordinates.
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* Support for rasterizing additional primitives:
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* (0.5) Lines.
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* (0.5) Line strips.
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* (0.5) Triangle fans.
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* (0.5) Triangle strips.
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* (0.5) Lines or line strips.
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* (0.5) Points.
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* (1.0) Anti-aliasing
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* (1.0) Occlusion queries
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* (1.0) Order-independent translucency using a k-buffer
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* (0.5) **Mouse**-based interactive camera support.
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* Fragment shading.
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* `fragmentIn[m] fs_input -> fragmentOut[m] fs_output`
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* A super-simple test fragment shader: output same color for every fragment.
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* Also try Tdisplaying various debug views (normals, etc.)
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* Also try displaying various debug views (normals, etc.)
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* Fragments to depth buffer.
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* `fragmentOut[m] -> fragmentOut[resolution]`
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* Can really be done inside the fragment shader.
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## README
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All students must replace or augment the contents of this Readme.md in a clear
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manner with the following:
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Replace the contents of this README.md in a clear manner with the following:
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* A brief description of the project and the specific features you implemented.
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* At least one screenshot of your project running.
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* A 30 second or longer video of your project running.
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* A performance evaluation (described in detail below).
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* A performance analysis (described below).
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### Performance Analysis
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The performance evaluation is where you will investigate how to make your CUDA
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The performance analysis is where you will investigate how to make your CUDA
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programs more efficient using the skills you've learned in class. You must have
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performed at least one experiment on your code to investigate the positive or
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negative effects on performance.

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