task06

Task06: Ambient Occlusion and BVH (Monte Carlo Integration, Importance Sampling)

Deadline: May 30th (Thu) at 15:00pm

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Before Doing Assignment

If you have not done the task01, task02 do it first to set up the C++ development environment.

Follow this document to submit the assignment, In a nutshell, before doing the assignment,

• make sure you synchronized the main branch of your local repository to that of remote repository.
• make sure you created branch task06 from main branch.
• make sure you are currently in the task06 branch (use git branch -a command).

Now you are ready to go!

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Problem 1 (compilation practice)

1. Build the code using cmake
2. Run the code with release mode

The program will output normal_map.png that update the image below

Problem 2 (efficient search using BVH)

The code in Problem 1 is very slow. It is because the computation of intersection betweeen a ray and a triangle mesh is brute force.

Comment out the brute force intersection computation (from line#146 to line#158) and implement code to evaluate BVH to accelerate the computation around line #120.

The program output the computation time. Fill the table below to compare the timing before/after the acceleraion. Please make sure that you build the code with release mode

brute force	BVH
??? ms	??? ms

Problem 3 (ambient occlusion)

Now you have the code for fast ray-mesh intersection. Using that code, let's compute the ambient occlusion.

• First, Comment out the continue; in the main() at line #21 in the original code . This will enable the ambient occlusion computation. The problem will output ao.png.

• Then fix the bug in line #223in the original code to correctly compute the ambient occlusion. The result should looks like preview.png at the begining of this document.

• Finally, modify the name of the outut image as ao_uniform.png

Problem 4 (importance sampling)

The computation of ambient occlusion is a bit noisy (i.e., the variance is high). Let's implement the importance sampling to reduce the variance. Write some code to compute the direction and PDF of **cosine-weighted sampling of hemisphere **around line #53. Modify the name of the output image ao.png as ao_cosweight.png.

Uniform sample	Cosine weighted sample

Observe that the variance (noise) is reduced using the importance sampling

After Doing the Assignment

After modify the code, push the code and submit a pull request. Make sure your pull request only contains the files you edited. Good luck!