Nebula engine

Get in contact on Discord! https://discord.gg/wuYPxUF

Check out the documentation (WIP) here: https://gscept.github.io/nebula-doc/

Requirements

1. OS: Windows and Linux(WIP)
2. Compiler with support for C++20.
3. GPU and drivers supporting Vulkan 1.2+
4. CMake 3.21+
5. Python 3.5+
   • Python requirements (Windows):
     1. Matching architecture (64-bit if you're building for 64-bit systems)
     2. Installed for all users
     3. Added to PATH
     4. Installed with debugging symbols and binaries

Setup

There are a few simple projects in the test folder, but a more interesting project is available as well Nebula-demo. Fips will take care of getting the relevant dependencies (including the actual engine in case you are setting up a external project)

Setup config

1. fips set config win64-vstudio-debug in your project directory, e.g. the cloned nebula-demo. There are other configs available, see in fips-files/configs
2. fips fetch Downloads/checks out all the other required repositories

Select the project and source directory

Run fips nebula verb to set work and toolkit directory registry variables:

• fips nebula set work {PATH} (If you are building an external project, this would be the current path)
• fips nebula set toolkit {PATH} (this is the path to where the nebula checkout resides, if an external project that would be ../nebula)

Build project

In your project directory:

1. fips physx build vc17 debug (if you are running VS 2022, use vc16 or vc15 for vs 2019/2017 instead)
2. fips anyfx setup
3. fips ultralight
4. fips gen to generate the required build system files, e.g. a visual studio solution
5. fips build to directly compile the project or fips open to open the generated solution in your selected environment

Features

Nebula is being developed continuously, which means that features keep getting added all the time. Currently, we support this:

• Completely data-driven design from bottom to top.
• Data structure suite, from containers to OS wrappers, everything is designed for performance and minimal call stacks.
• Multithreading.
• SSE-accelerated and intuitive maths library.
• Full python supported scripting layer.
• Advanced rendering framework and shaders.
• Test-benches and benchmarking.
• Profiling tools.

Rendering

A lot of effort has been made to the Nebula rendering subsystem, where we currently support:

• Unified clustering system - fog volumes, decals and lights all go into the same structure.
• Screen-space reflections - working condition, but still work in progress.
• Horizon-based ambient occlusion done in compute.
• Physically based materials and rendering.
• Multi-threaded subpass recording.
• Shadow mapping for local lights and CSM for global/directional/sun light.
• Volumetric fog and lighting.
• Geometric decals.
• CPU-GPU hybrid particle system.
• Skinning and animation.
• Scripted rendering path.
• Vulkan.
• Tonemapping.
• Asynchronous compute.
• Virtual texturing using sparse binding.
• Adaptive virtual textured terrain.
• Fast and conservative GPU memory allocation.
• Area lights.

Entity system

Nebula has historically had a database-centric approach to entities. With the newest iteration of Nebula, we've decided to keep improving by adopting an ECS approach, still keeping it database-centric.

• Data-oriented
• Data-driven
• Minimal memory overhead per entity.
• High performance without compromising usability or simplicity
• Blueprint and template system for easily instantiating and categorizing entity types.
• Automatic serialization and deserialization

Screenshots

Deferred Lighting using 3D clustering and GPU culling. Geometric decals, culled on GPU and rendered in screen-space. Volumetric fog lighting. Local fog volumes. Profiling tools.