Yocto/GL is a collection of small C++17 libraries for building physically-based graphics algorithms released under the MIT license. Yocto/GL is written in a deliberately data-oriented style for ease of development and use. Yocto/GL is split into small libraries to make code navigation easier. See each header file for documentation.

• yocto/yocto_math.{h}: fixed-size vectors, matrices, rigid frames, transforms
• yocto/yocto_color.{h}: color conversion, color adjustment, tone mapping functions, color grading, color maps, color spaces
• yocto/yocto_geometry.{h}: rays, bounding boxes, geometry functions, ray-primitive intersection, point-primitive overlap
• yocto/yocto_noise.{h}: Perlin noise
• yocto/yocto_sampling.{h}: random number generation, generation of points and directions, Monte Carlo utilities
• yocto/yocto_shading.{h}: evaluation and sampling of fresnel functions, bsdf lobes, transmittance lobes, phase functions
• yocto/yocto_image.{h,cpp}: simple image data type, image resizing, tonemapping, color correction, procedural images, procedural sun-sky
• yocto/yocto_shape.{h,cpp}: utilities for manipulating triangle meshes, quads meshes and line sets, computation of normals and tangents, linear and Catmull-Clark subdivision, procedural shapes generation, ray intersection and closest point queries
• yocto/yocto_mesh.{h,cpp}: computational geometry utilities for triangle meshes, mesh geodesic, mesh cutting
• yocto/yocto_bvh.{h,cpp}: ray intersection and closest point queries of triangle meshes, quads meshes, line sets and instances scenes using a two-level bounding volume hierarchy
• yocto/yocto_scene.{h,cpp}: scene representation and properties evaluation
• yocto/yocto_sceneio.{h,cpp}: image serialization, shape serialization, scene serialization, text and binary serialization, path helpers
• yocto/yocto_trace.{h,cpp}: path tracing of surfaces and hairs supporting area and environment illumination, microfacet GGX and subsurface scattering, multiple importance sampling
• yocto/yocto_modelio.{h,cpp}: parsing and writing for Ply, Obj, Stl, Pbrt formats
• yocto/yocto_cli.h: printing utilities, command line parsing
• yocto/yocto_parallel.h: concurrency utilities

You can see Yocto/GL in action in the following applications written to test the library:

• apps/yscene.cpp: command-line scene manipulation and rendering, and interactive viewing
• apps/yshape.cpp: command-line shape manipulation and rendering, and interactive viewing
• apps/yimage.cpp: command-line image manipulation, and interactive viewing

Here are some test images rendered with the path tracer. More images are included in the project site.

Yocto/GL follows a "data-oriented programming model" that makes data explicit. Data is stored in simple structs and accessed with free functions or directly. All data is public, so we make no attempt at encapsulation. Most objects is Yocto/GL have value semantic, while large data structures use reference semantic with strict ownership. This means that everything can be trivially serialized and there is no need for memory management.

We do this since this makes Yocto/GL easier to extend and quicker to learn, with a more explicit data flow that is easier when writing parallel code. Since Yocto/GL is mainly used for research and teaching, explicit data is both more hackable and easier to understand.

In terms of code style we prefer a functional approach rather than an object oriented one, favoring free functions to class methods. All functions and data are defined in sibling namespaces contained in the yocto namespace so libraries can call all others, but have to do so explicitly.

The use of templates in Yocto was the reason for many refactoring, going from no template to heavy template use. At this point, Yocto uses some templates for readability. In the future, we will increase the use of templates in math code, while keeping many APIs explicitly typed.

We do not use exception for error reporting, but only to report "programmers" errors. For example, IO operations use boolean flags and error strings for human readable errors, while exceptions are used when preconditions or post conditions are violated in functions.

After several refactoring, we settled on a value-based approach, since the use of pointers and reference semantics was hard for many of our users. While this has the drawback of potentially introducing spurious copies, it does have th benefit of ensuring that no memory corruption can occur, which turned out was a major issue for novice C++ users, even in a very small library like this one.

Main contributors:

• Fabio Pellacini (lead developer): web, github
• Edoardo Carra: github
• Giacomo Nazzaro: github

This library includes code from the PCG random number generator, boost hash_combine, and public domain code from github.com/sgorsten/linalg, gist.github.com/badboy/6267743 and github.com/nothings/stb_perlin.h. Other external libraries are included with their own license.

This library requires a C++17 compiler and is know to compiled on OsX (Xcode >= 11), Windows (MSVC 2019) and Linux (gcc >= 9, clang >= 9).

You can build the example applications using CMake with mkdir build; cd build; cmake ..; cmake --build .

Yocto/GL depends on stb_image.h, stb_image_write.h, stb_image_resize.h and tinyexr.h for image loading, saving and resizing, cgltf.h and json.hpp for glTF and JSON support, and filesystem.hpp to support C++17 filesystem API when missing. All dependencies are included in the distribution.

Yocto/GL optionally supports building OpenGL demos, which are handled by including glad, GLFW, ImGui as dependencies in apps. OpenGL support might eventually become part of the Yocto/GL libraries. OpenGL support is enabled by defining the cmake option YOCTO_OPENGL and contained in the yocto_gui library.

Yocto/GL optionally supports the use of Intel's Embree for ray casting. See the main CMake file for how to link to it. Embree support is enabled by defining the cmake option YOCTO_EMBREE.

Yocto/GL optionally supports the use of Intel's Open Image Denoise for denoising. See the main CMake file for how to link to it. Open Image Denoise support is enabled by defining the cmake option YOCTO_DENOISE.