DXR Demo - Michael Hamm

  Code so Far Here is the  code  for this section Denoising Last time I implemented a path tracer that used importance sampling for optimization. This week, to wrap up this project I added a denoiser. When it comes to denoising there is a couple approaches. They are described here  but they are filtering technique, machine learning techniques, and sampling techniques. Filtering techniques are cheap but blur the image. Machine learning uses autoencoders to reconstruct images from noisy images. Sampling techniques use spatial and temporal data to denoise the image. Because, I am not denoising the image per frame I wouldn't be able to take advantage of spatial temporal solutions like Nvidia's real time denoiser (NRD). NRD would have been a better solution than machine learning however due to its performance. For my program I opted for a machine learning encoder. There were multiple to choose from the most popular were Nvidia's Optix, and Intel's Open Image Denoise (OIDN).

  Code so Far Here is the  code  for this section Switch to Physically Based Rendering (PBR) Cont. Last time I began implementing the Monte Carlo estimator on the ray tracer, but did not complete it. This is what the results looked like from last week. After realizing my seed was blatantly wrong for my random function I was quickly able to achieve closer results to what I was expecting: The image above uses 50,000 samples per pixel with 2 bounces. This runs at 1fps. With 100,000 samples per pixel I am able to get an image with almost no noise.  100,000 Samples Above is the render with 100,000 samples. Ever with this many samples I am still getting noise. Additionally if you observe the noise in both the 50,000 samples and 100,000 samples, the noise is not uniform. This indicates that either my function that maps a 2d sample over a sphere is incorrect or the generation of the random 2d sample itself is incorrect. After debugging this I was able to observe it was the random seed again an

  Code so Far Here is the  code  for this section Switch to Physically Based Rendering (PBR) The scene is setup with basic lighting, and it is running pretty fast (thousands of fps), but we aren't using the power of raytracing other than generating shadows. To really show off my ray tracer I want to support global illumination. What I wanted to focus on this week was switching over to a more realistic model of how light works. For this, I needed to implement a model that estimates the rendering equation. For reference, the rendering equation is , and the part under the integral is the equation that needs to be estimated. Monte Carlo  My first attempt at estimating will be to implement a Monte Carlo estimator, Fn. What the Monte Carlo algorithm will do is for every surface position hit by a ray, a new ray will get fired in a random direction. The process of firing rays will continue recursively until a maximum number of hits is reached. I will performance this process multiple times

Code so Far Here is the  code  for this section Rendering a Scene Now that I can render a scene, and have the raytracing pipeline setup, I want to make the raytracing scene look better. In rasterization there is diffuse light, specular lighting, etc., but how do you do proper shading for a ray tracer? To answer this question I picked up the book  Physically Based Rendering, Fourth Edition , by Matt Phar et al. This book goes in depth in the theory and implementation of how to achieve photorealistic renderers using raytracing. A critical understanding of how light actually works is necessary (at least at a macro level) in order to replicate its behavior. When a light source strikes an object it's light can either be reflected, refracted, or scattered in any direction due to subsurface scattering. The probability of each direction can be described by a probability distribution called bidirection scattering distribution function (BSDF).  Surface scattering example (Matt Phar) In the e