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Project 3 CUDA Path Tracer - Instructions

This is due Tuesday October 1st at 11:59pm.

This project involves a significant bit of running time to generate high-quality images, so be sure to take that into account. You will receive an additional 2 days (due Thursday, October 3rd) for "README and Scene" only updates. However, the standard project requirements for READMEs still apply for the October 1st deadline. You may use these two extra days to improve your images, charts, performance analysis, etc.

If you plan to use late days on this project (which we recommend), they can be applied to the code deadline (which will also push back the README deadline), or they can be applied to just the README deadline. For example, you can use one late day to push the code deadline to October 2nd and the README deadline to October 4th, and then use another late day to push the README deadline further to October 5th, for a total of two late days used.

Link to "Pathtracing Primer" slides

Summary:

In this project, you'll implement a CUDA-based path tracer capable of rendering globally-illuminated images very quickly. Since in this class we are concerned with working in GPU programming, performance, and the generation of actual beautiful images (and not with mundane programming tasks like I/O), this project includes base code for loading a scene description file, described below, and various other things that generally make up a framework for previewing and saving images.

The core renderer is left for you to implement. Finally, note that, while this base code is meant to serve as a strong starting point for a CUDA path tracer, you are not required to use it if you don't want to. You may change any part of the base code as you please, or even start from scratch. This is YOUR project.

Recommendations:

  • Every image you save should automatically get a different filename. Don't delete all of them! For the benefit of your README, keep a bunch of them around so you can pick a few to document your progress at the end. Outtakes are highly appreciated!
  • Remember to save your debug images - these will make for a great README.
  • Also remember to save and share your bloopers. Every image has a story to tell and we want to hear about it.

Contents

  • src/ C++/CUDA source files.
  • scenes/ Example scene description JSON files.
  • img/ Renders of example scene description files. (These probably won't match precisely with yours.)
  • external/ Includes and static libraries for 3rd party libraries.

Running the code

The main function requires a scene description file. Call the program with one as an argument: cis565_path_tracer scenes/sphere.json. (In Visual Studio, ../scenes/sphere.json.)

If you are using Visual Studio, you can set this in the Debugging > Command Arguments section in the Project Properties. Make sure you get the path right - read the console for errors.

Controls

  • Esc to save an image and exit.
  • S to save an image. Watch the console for the output filename.
  • Space to re-center the camera at the original scene lookAt point.
  • Left mouse button to rotate the camera.
  • Right mouse button on the vertical axis to zoom in/out.
  • Middle mouse button to move the LOOKAT point in the scene's X/Z plane.

Requirements

In this project, you are given code for:

  • Loading and reading the scene description format.
  • Sphere and box intersection functions.
  • Support for saving images.
  • Working CUDA-GL interop for previewing your render while it's running.
  • A skeleton renderer with:
    • Naive ray-scene intersection.
    • A "fake" shading kernel that colors rays based on the material and intersection properties but does NOT compute a new ray based on the BSDF.

Ask in Ed Discussion for clarifications.

Part 1 - Core Features

You will need to implement the following features:

  • A shading kernel with BSDF evaluation for:
    • Ideal diffuse surfaces (using provided cosine-weighted scatter function, see below.) PBRTv4 9.2
    • Perfectly specular-reflective (mirrored) surfaces (e.g. using glm::reflect).
    • See notes on diffuse/specular in scatterRay and on imperfect specular below.
  • Path continuation/termination using Stream Compaction from Project 2.
  • After you have a basic pathtracer up and running, implement a means of making rays/pathSegments/intersections contiguous in memory by material type. This should be easily toggleable.
    • Consider the problems with coloring every path segment in a buffer and performing BSDF evaluation using one big shading kernel: different materials/BSDF evaluations within the kernel will take different amounts of time to complete.
    • Sort the rays/path segments so that rays/paths interacting with the same material are contiguous in memory before shading. How does this impact performance? Why?
  • Lastly, implement stochastic sampled antialiasing. See the "Stochastic Sampling" section in Paul Bourke's notes.

Part 2 - Make Your Pathtracer Unique!

The following features are a non-exhaustive list of features you can choose from based on your own interests and motivation. Each feature has an associated score (represented in emoji numbers, eg. 5️⃣).

You are required to implement additional features of your choosing from the list below totalling up to minimum 10 score points.

An example set of optional features is:

  • Mesh Loading - 4️⃣ points
  • Refraction - 2️⃣ points
  • Depth of field - 2️⃣ points
  • Final rays post processing - 3️⃣ points

This list is not comprehensive. If you have a particular idea you would like to implement (e.g. acceleration structures, etc.), please post on Ed.

Extra credit: implement more features on top of the above required ones, with point value up to +25/100 at the grader's discretion (based on difficulty and coolness), generally.

Visual Improvements

  • 2️⃣ Refraction (e.g. glass/water) PBRTv4 9.3 with Frensel effects using Schlick's approximation or more accurate methods PBRTv4 9.5. You can use glm::refract for Snell's law.
    • Recommended but not required: non-perfect specular surfaces (see "Imperfect Specular Lighting" below).
  • 2️⃣ Physically-based depth-of-field (by jittering rays within an aperture). PBRTv4 5.2.3
  • 4️⃣ Procedural Shapes & Textures.
    • You must generate a minimum of two different complex shapes procedurally. (Not primitives)
    • You must be able to shade object with a minimum of two different textures
  • 5️⃣ (6️⃣ if combined with Arbitrary Mesh Loading) Texture mapping PBRTv4 10.4 and bump mapping PBRTv3 9.3.
    • Implement file-loaded textures AND a basic procedural texture
    • Provide a performance comparison between the two
  • 2️⃣ Direct lighting by taking a final ray directly to a random point on an emissive object acting as a light source). Or more advanced PBRTv4 13.4.
  • 4️⃣ Subsurface scattering PBRTv3 5.6.2, 11.4.
  • 3️⃣ Better random number sequences for Monte Carlo ray tracing
  • 3️⃣ Some method of defining object motion, and motion blur by averaging samples at different times in the animation.
  • 3️⃣ Use final rays to apply post-processing shaders. Please post your ideas on Piazza before starting.

Mesh Improvements

  • Arbitrary mesh loading and rendering (e.g. glTF 2.0 (preferred) or obj files) with toggleable bounding volume intersection culling
    • 4️⃣ glTF
    • 2️⃣ OBJ
    • For other formats, please check on the class forum
    • You can find models online or export them from your favorite 3D modeling application. With approval, you may use a third-party loading code to bring the data into C++.
      • tinygltf is highly recommended for glTF.
      • tinyObj is highly recommended for OBJ.
      • obj2gltf can be used to convert OBJ to glTF files. You can find similar projects for FBX and other formats.
    • You can use the triangle intersection function glm::intersectRayTriangle.
    • Bounding volume intersection culling: reduce the number of rays that have to be checked against the entire mesh by first checking rays against a volume that completely bounds the mesh. For full credit, provide performance analysis with and without this optimization.

    Note: This goes great with the Hierarcical Spatial Data Structures.

Performance Improvements

  • 1️⃣ Implement Russian roulette path termination, which terminates unimportant paths early without introducing bias. Make sure to include a performance evaluation with and without it enabled, especially for closed scenes. PBRTv3 13.7
  • 2️⃣ Work-efficient stream compaction using shared memory across multiple blocks. (See GPU Gems 3, Chapter 39.)
    • Note that you will NOT receieve extra credit for this if you implemented shared memory stream compaction as extra credit for Project 2.
  • 6️⃣ Hierarchical spatial data structures - for better ray/scene intersection testing
    • BVH or Octree recommended - this feature is more about traversal on the GPU than perfect tree structure
    • CPU-side data structure construction is sufficient - GPU-side construction was a final project.
    • Make sure this is toggleable for performance comparisons
    • If implemented in conjunction with Arbitrary mesh loading (required for this year), this qualifies as the toggleable bounding volume intersection culling.
    • See below for more resources
  • 6️⃣ Wavefront pathtracing: Group rays by material without a sorting pass. A sane implementation will require considerable refactoring, since every supported material suddenly needs its own kernel.
  • 3️⃣ Open Image AI Denoiser or an alternative approve image denoiser Open Image Denoiser is an image denoiser which works by applying a filter on Monte-Carlo-based pathtracer output. The denoiser runs on the CPU and takes in path tracer output from 1spp to beyond. In order to get full credit for this, you must pass in at least one extra buffer along with the raw "beauty" buffer. Ex: Beauty + Normals.
    • Part of this extra credit is figuring out where the filter should be called, and how you should manage the data for the filter step.
    • It is important to note that integrating this is not as simple as it may seem at first glance. Library integration, buffer creation, device compatibility, and more are all real problems which will appear, and it may be hard to debug them. Please only try this if you have finished the Part 2 early and would like extra points. While this is difficult, the result would be a significantly faster resolution of the path traced image.
  • 5️⃣ Re-startable Path tracing: Save some application state (iteration number, samples so far, acceleration structure) so you can start and stop rendering instead of leaving your computer running for hours at end (which will happen in this project)
  • 5️⃣ Switch the project from using CUDA-OpenGL Interop to using CUDA-Vulkan interop (this is a really great one for those of you interested in doing Vulkan). Talk to TAs if you are planning to pursue this.

Optimization

For those of you that are not as interested in the topic of rendering, we encourage you to focus on optimizing the basic path tracer using GPU programming techniques and more advanced CUDA features. In addition to the core features, we do recommend at least implementing an OBJ mesh loader before focusing on optimization so that you can load in heavy geometries to start seeing performance hit. Please refer to the course materials (especially the CUDA Performance lecture) and the CUDA's Best Practice Guide on how to optimize CUDA performance. Some examples include:

  • Use shared memory to improve memory bandwidth
  • Use intrinsinc functions to improve instruction throughput
  • Use CUDA streams and/or graph for concurrent kernel executions

For each specific optimization technique, please post on Ed Discussion so we can determine the appropriate points to award.

Analysis

For each extra feature, you must provide the following analysis:

  • Overview write-up of the feature along with before/after images.
  • Performance impact of the feature.
  • If you did something to accelerate the feature, what did you do and why?
  • Compare your GPU version of the feature to a HYPOTHETICAL CPU version (you don't have to implement it!). Does it benefit or suffer from being implemented on the GPU?
  • How might this feature be optimized beyond your current implementation?

Base Code Tour

You'll be working in the following files. Look for important parts of the code:

  • Search for CHECKITOUT.

  • You'll have to implement parts labeled with TODO. (But don't let these constrain you - you have free rein!)

  • src/pathtrace.h/cu: path tracing kernels, device functions, and calling code

    • pathtraceInit initializes the path tracer state - it should copy scene data (e.g. geometry, materials) from Scene.
    • pathtraceFree frees memory allocated by pathtraceInit
    • pathtrace performs one iteration of the rendering - it handles kernel launches, memory copies, transferring some data, etc.
      • See comments for a low-level path tracing recap.

  • src/intersections.h/cu: ray intersection functions

    • boxIntersectionTest and sphereIntersectionTest, which take in a ray and a geometry object and return various properties of the intersection.

  • src/interactions.h/cu: ray scattering functions

    • calculateRandomDirectionInHemisphere: a cosine-weighted random direction in a hemisphere. Needed for implementing diffuse surfaces.
    • scatterRay: this function should perform all ray scattering, and will call calculateRandomDirectionInHemisphere. See comments for details.

  • src/main.cpp: you don't need to do anything here, but you can change the program to save .hdr image files, if you want (for postprocessing).

  • stream_compaction: A dummy folder into which you should place your Stream Compaction implementation from Project 2. It should be sufficient to copy the files from here

Generating random numbers

thrust::default_random_engine rng(hash(index));
thrust::uniform_real_distribution<float> u01(0, 1);
float result = u01(rng);

There is a convenience function for generating a random engine using a combination of index, iteration, and depth as the seed:

thrust::default_random_engine rng = makeSeededRandomEngine(iter, index, path.remainingBounces);

Imperfect specular lighting

In path tracing, like diffuse materials, specular materials are simulated using a probability distribution instead computing the strength of a ray bounce based on angles.

Equations 7, 8, and 9 of GPU Gems 3, Chapter 20 give the formulas for generating a random specular ray. (Note that there is a typographical error: χ in the text = ξ in the formulas.)

Also see the notes in scatterRay for probability splits between diffuse/specular/other material types.

See also: PBRTv3 8.2.2.

Hierarchical spatial datastructures

One method for avoiding checking a ray against every primitive in the scene or every triangle in a mesh is to bin the primitives in a hierarchical spatial datastructure such as an octree.

Ray-primitive intersection then involves recursively testing the ray against bounding volumes at different levels in the tree until a leaf containing a subset of primitives/triangles is reached, at which point the ray is checked against all the primitives/triangles in the leaf.

  • We highly recommend building the datastructure on the CPU and encapsulating the tree buffers into their own struct, with its own dedicated GPU memory management functions.
  • We highly recommend working through your tree construction algorithm by hand with a couple cases before writing any actual code.
    • How does the algorithm distribute triangles uniformly distributed in space?
    • What if the model is a perfect axis-aligned cube with 12 triangles in 6 faces? This test can often bring up numerous edge cases!
  • Note that traversal on the GPU must be coded iteratively!
  • Good execution on the GPU requires tuning the maximum tree depth. Make this configurable from the start.
  • If a primitive spans more than one leaf cell in the datastructure, it is sufficient for this project to count the primitive in each leaf cell.

Handling Long-Running CUDA Threads

By default, your GPU driver will probably kill a CUDA kernel if it runs for more than 5 seconds. There's a way to disable this timeout. Just beware of infinite loops - they may lock up your computer.

The easiest way to disable TDR for Cuda programming, assuming you have the NVIDIA Nsight tools installed, is to open the Nsight Monitor, click on "Nsight Monitor options", and under "General" set "WDDM TDR enabled" to false. This will change the registry setting for you. Close and reboot. Any change to the TDR registry setting won't take effect until you reboot. Stack Overflow

Scene File Format

Note: The Scene File Format and sample scene files are provided as a starting point. You are encouraged to create your own unique scene files, or even modify the scene file format in its entirety. Be sure to document any changes in your readme.

This project uses a JSON-based scene description format to define all components of a scene, such as materials, objects, lights, and camera settings. The scene file is structured as a JSON object with clearly organized sections for different elements, providing a clean and extendable format.

Materials

Materials are defined under the "Materials" section. Each material have a unique name and belongs to a material type such as "Diffuse", "Specular", or "Emitting".

For each type of material, it can have different properties such as:

  • "RGB": An array of three float values defining the material’s color.
  • "EMITTANCE": A float value for emissive materials, which defines the light emission strength (optional, present only for emitting materials).
  • "ROUGHNESS": A float value indicating surface roughness, used for specular materials.

Example:

"diffuse_red": {
    "TYPE": "Diffuse",
    "RGB": [0.85, 0.35, 0.35]
}

Camera

The camera configuration is defined in the "Camera" section. It includes settings for resolution, field of view, iterations for rendering, and camera orientation.

  • "RES": An array representing the resolution of the output image in pixels.
  • "FOVY": The vertical field of view, in degrees.
  • "ITERATIONS": The number of iterations to refine the image during rendering.
  • "DEPTH": The maximum path tracing depth.
  • "FILE": The filename for the rendered output.
  • "EYE": The position of the camera in world coordinates.
  • "LOOKAT": The point in space the camera is directed at.
  • "UP": The up vector defining the camera's orientation.

Example:

"Camera": {
    "RES": [800, 800],
    "FOVY": 45.0,
    "ITERATIONS": 5000,
    "DEPTH": 8,
    "FILE": "cornell",
    "EYE": [0.0, 5.0, 10.5],
    "LOOKAT": [0.0, 5.0, 0.0],
    "UP": [0.0, 1.0, 0.0]
}

Objects

Objects in the scene are defined as an array of entries under the "Objects" section. Each object contains:

  • "TYPE": The type of object, such as "cube" or "sphere".
  • "MATERIAL": The material assigned to the object, referencing one of the materials defined earlier.
  • "TRANS": An array for the translation (position) of the object.
  • "ROTAT": An array for the rotation of the object in degrees.
  • "SCALE": An array for the scale of the object.

Example:

{
    "TYPE": "cube",
    "MATERIAL": "diffuse_red",
    "TRANS": [-5.0, 5.0, 0.0],
    "ROTAT": [0.0, 0.0, 0.0],
    "SCALE": [0.01, 10.0, 10.0]
}

This JSON format is flexible and can be easily extended to accommodate new features, such as additional material properties or object types.

Third-Party Code Policy

  • Use of any third-party code must be approved by asking on our Ed Discussion.
  • 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 path 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, at minimum, result in you receiving an F for the semester.
  • You may use third-party 3D models and scenes in your projects. Be sure to provide the right attribution as requested by the creators.

README

Please see: TIPS FOR WRITING AN AWESOME README

  • Sell your project.
  • Assume the reader has a little knowledge of path tracing - don't go into detail explaining what it is. Focus on your project.
  • Don't talk about it like it's an assignment - don't say what is and isn't "extra" or "extra credit." Talk about what you accomplished.
  • Use this to document what you've done.
  • Your cover image should NOT be a Cornell box - show something more interesting!
    • If you are heavily customizing it, seek pre-approval via Ed Discussion.
  • DO NOT leave the README to the last minute!
    • It is a crucial part of the project, and we will not be able to grade you without a good README.
    • Generating images will take time. Be sure to account for it!

In addition:

  • This is a renderer, so include images that you've made!
  • Be sure to back your claims for optimization with numbers and comparisons.
  • If you reference any other material, please provide a link to it.
  • You wil not be graded on how fast your path tracer runs, but getting close to real-time is always nice!
  • If you have a fast GPU renderer, it is very good to show case this with a video to show interactivity. If you do so, please include a link!

Analysis

  • Stream compaction helps most after a few bounces. Print and plot the effects of stream compaction within a single iteration (i.e. the number of unterminated rays after each bounce) and evaluate the benefits you get from stream compaction.

  • Compare scenes which are open (like the given cornell box) and closed (i.e. no light can escape the scene). Again, compare the performance effects of stream compaction! Remember, stream compaction only affects rays which terminate, so what might you expect?

  • For optimizations that target specific kernels, we recommend using stacked bar graphs to convey total execution time and improvements in individual kernels. For example:

    Clearly the Macchiato is optimal.

    Timings from NSight should be very useful for generating these kinds of charts.

Submit

If you have modified any of the CMakeLists.txt files at all (aside from the list of SOURCE_FILES), mentions it explicity.

Beware of any build issues discussed on the Piazza.

Open a GitHub pull request so that we can see that you have finished.

The title should be "Project 3: YOUR NAME".

The template of the comment section of your pull request is attached below, you can do some copy and paste:

  • Repo Link
  • (Briefly) Mentions features that you've completed. Especially those bells and whistles you want to highlight
    • Feature 0
    • Feature 1
    • ...
  • Feedback on the project itself, if any.

References