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a documentation theme for Jekyll. </footer></div><divclass="main"id="top"><divid="main-header"class="main-header"><divclass="search"><divclass="search-input-wrap"><inputtype="text"id="search-input"class="search-input"tabindex="0"placeholder="Search "aria-label="Search "autocomplete="off"><labelfor="search-input"class="search-label"><svgviewBox="0 0 24 24"class="search-icon"><usexlink:href="#svg-search"></use></svg></label></div><divid="search-results"class="search-results"></div></div></div><divid="main-content-wrap"class="main-content-wrap"><navaria-label="Breadcrumb"class="breadcrumb-nav"><olclass="breadcrumb-nav-list"><liclass="breadcrumb-nav-list-item"><ahref="/pathtracer/">A3: Pathtracer</a></li><liclass="breadcrumb-nav-list-item"><span>(Task 5) Path Tracing</span></li></ol></nav><divid="main-content"class="main-content"role="main"><h1id="task-5-path-tracing"><ahref="#task-5-path-tracing"class="anchor-heading"aria-labelledby="task-5-path-tracing"><svgviewBox="0 0 16 16"aria-hidden="true"><usexlink:href="#svg-link"></use></svg></a> (Task 5) Path Tracing </h1><p>Up to this point, your renderer simulates light which begins at a source, bounces off a surface, and hits a camera. However in the real world, light can take much more complicated paths, bouncing of many surfaces before eventually reaching the camera. Simulating this multi-bounce light is referred to as <em>indirect illumination</em>, and it is critical to producing realistic images, especially when specular surfaces are present. In this task you will modify your ray tracer to simulate multi-bounce light, adding support for indirect illumination.</p><p>You must modify <codeclass="language-plaintext highlighter-rouge">Pathtracer::trace_ray</code> to simulate multiple bounces. We recommend using the <ahref="http://15462.courses.cs.cmu.edu/spring2020/lecture/montecarloraytracing/slide_044">Russian Roulette</a> algorithm discussed in class.</p><p>The basic structure will be as follows:</p><ul><li>(1) Randomly select a new ray direction using <codeclass="language-plaintext highlighter-rouge">bsdf.sample</code> (which you will implement in Step 2)</li><li>(2) Potentially terminate the path (using Russian roulette)</li><li>(3) Recursively trace the ray to evaluate weighted reflectance contribution due to light from this direction. Remember to respect the maximum number of bounces from <codeclass="language-plaintext highlighter-rouge">max_depth</code> (which is a member of class <codeclass="language-plaintext highlighter-rouge">Pathtracer</code>). Don’t forget to add in the BSDF emissive component!</li></ul><h2id="step-2"><ahref="#step-2"class="anchor-heading"aria-labelledby="step-2"><svgviewBox="0 0 16 16"aria-hidden="true"><usexlink:href="#svg-link"></use></svg></a> Step 2 </h2><p>Implement <codeclass="language-plaintext highlighter-rouge">BSDF_Lambertian::sample</code> for diffuse reflections, which randomly samples a direction from a uniform hemisphere distribution and returns a <codeclass="language-plaintext highlighter-rouge">BSDF_Sample</code>. Note that the interface is in <codeclass="language-plaintext highlighter-rouge">rays/bsdf.h</code>. Task 6 contains further discussion of sampling BSDFs, reading ahead may help your understanding. The implementation of <codeclass="language-plaintext highlighter-rouge">BSDF_Lambertian::evaluate</code> is already provided to you.</p><p>Note:</p><ul><li><p>When adding the recursive term to the total radiance, you will need to account for emissive materials, like the ceiling light in the Cornell Box (cbox.dae). To do this, simply add the BSDF sample’s emissive term to your total radiance, i.e. <codeclass="language-plaintext highlighter-rouge">L += sample.emisssive</code>.</p></li><li><p>Functions in <codeclass="language-plaintext highlighter-rouge">student/sampler.cpp</code> from class <codeclass="language-plaintext highlighter-rouge">Sampler</code> contains helper functions for random sampling, which you will use for sampling. Our starter code uses uniform hemisphere sampling <codeclass="language-plaintext highlighter-rouge">Samplers::Hemisphere::Uniform sampler</code>(see <codeclass="language-plaintext highlighter-rouge">rays/bsdf.h</code> and <codeclass="language-plaintext highlighter-rouge">student/sampler.cpp</code>) which is already implemented. You are welcome to implement Cosine-Weighted Hemisphere sampling for extra credit, but it is not required. If you want to implement Cosine-Weighted Hemisphere sampling, fill in <codeclass="language-plaintext highlighter-rouge">Hemisphere::Cosine::sample</code> in <codeclass="language-plaintext highlighter-rouge">student/samplers.cpp</code> and then change <codeclass="language-plaintext highlighter-rouge">Samplers::Hemisphere::Uniform sampler</code> to <codeclass="language-plaintext highlighter-rouge">Samplers::Hemisphere::Cosine sampler</code> in <codeclass="language-plaintext highlighter-rouge">rays/bsdf.h</code>.</p></li></ul><hr/><p>After correctly implementing path tracing, your renderer should be able to make a beautifully lit picture of the Cornell Box. Below is the rendering result of 1024 sample per pixel.</p><p><imgsrc="new_results/lambertian.png"alt="cornell_lambertian"/></p><p>Note the time-quality tradeoff here. With these commandline arguments, your path tracer will be running with 8 worker threads at a sample rate of 1024 camera rays per pixel, with a max ray depth of 4. This will produce an image with relatively high quality but will take quite some time to render. Rendering a high quality image will take a very long time as indicated by the image sequence below, so start testing your path tracer early! Below are the result and runtime of rendering cornell box with different sample per pixel at 640 by 430 on Macbook Pro(3.1 GHz Dual-Core Intel Core i5).</p><p><imgsrc="new_results/timing.png"alt="spheres"/></p><p>Also note that if you have enabled Russian Roulette, your result may seem noisier, but should complete faster. The point of Russian roulette is not to increase sample quality, but to allow the computation of more samples in the same amount of time, resulting in a higher quality result.</p><p>Here are a few tips:</p><ul><li>The path termination probability should be computed based on the <ahref="http://15462.courses.cs.cmu.edu/fall2015/lecture/globalillum/slide_044">overall throughput</a> of the path. The throughput of the ray is recorded in its <codeclass="language-plaintext highlighter-rouge">throughput</code> member, which represents the multiplicative factor the current radiance will be affected by before contributing to the final pixel color. Hence, you should both use and update this field. To update it, simply multiply in the rendering equation factors: BSDF attenuation, <codeclass="language-plaintext highlighter-rouge">cos(theta)</code>, and (inverse) BSDF PDF. Remember to apply the coefficients from the current step before deriving the termination probability. Finally, note that the updated throughput should be copied to the recursive ray for later steps.</li></ul><p>Keep in mind that delta function BSDFs can take on values greater than one, so clamping termination probabilities derived from BSDF values to 1 is wise.</p><ul><li><p>To convert a Spectrum to a termination probability, we recommend you use the luminance (overall brightness) of the Spectrum, which is available via <codeclass="language-plaintext highlighter-rouge">Spectrum::luma</code></p></li><li><p>We’ve given you some <ahref="http://15462.courses.cs.cmu.edu/fall2015/lecture/globalillum/slide_047">pretty good notes</a> on how to do this part of the assignment, but it can still be tricky to get correct.</p></li></ul></div></div><divclass="search-overlay"></div></div></body></html>
