EvoEngine
May 15, 2026 ยท View on GitHub
EvoEngine is a C++17 research framework for interactive simulation, digital forestry, digital agriculture, synthetic dataset generation, and Vulkan rendering. The repository is built around a general-purpose SDK, compile-time domain Services, and a new runtime package layer. The SDK provides the application runtime, editor, ECS, renderer, asset system, serialization, and automation hooks; Services add research workflows at build time; runtime packages are shared-library modules that can be loaded while the app is running.
Windows is the primary development platform. Linux builds are supported for the core stack, while several Services are Windows-only or require optional SDKs.

1. EvoEngine SDK
The SDK is the foundation of the framework. It lives in EvoEngine_SDK and is responsible for the reusable engine/runtime systems that Services and applications build on.
SDK Responsibilities
The SDK provides:
- application lifecycle and layer composition
- scene and entity management
- a hybrid ECS with data components and private components
- systems, transforms, hierarchy, prefabs, and scene cloning
- project, folder, file, asset, and metadata management
- YAML-based serialization and type registration
- an ImGui editor layer for scene, entity, asset, console, and inspector workflows
- Vulkan platform setup and rendering infrastructure
- global geometry and texture storage
- material, mesh, camera, light, render texture, and post-processing assets/components
- job scheduling, input events, and frame/fixed-step timing
- resource copying and shader include registration support for Services
- runtime package loading, guarded unloading/reloading, and package-owned private component registration
Repository Layout
| Path | Purpose |
|---|---|
EvoEngine_SDK | Core runtime, ECS, editor, renderer, assets, serialization, jobs, input, and utilities. |
EvoEngine_Services | Build-time domain modules that extend the SDK and are linked into apps/Python bindings. |
EvoEngine_Packages | Runtime package shared-library modules loaded from Packages folders. |
EvoEngine_App | Executable apps that choose SDK layers and startup runtime packages. |
PythonBinding | pybind11 modules for scripted workflows. |
Resources | Demo projects, screenshots, textures, scripts, and build helpers. |
Extern | Vendored third-party libraries and submodules. |
cmake | CMake helper modules. |
Application Model
An EvoEngine app is assembled by pushing layers before initialization. A typical interactive app uses:
RenderLayerfor Vulkan rendering, render instance preparation, and external render callbacks.WindowLayerfor GLFW windows, input callbacks, resize handling, and presentation.EditorLayerfor ImGui tools, scene views, entity hierarchy, inspectors, asset browser, and console.- Runtime package layers such as
EcoSysLabLayer,SorghumLayer, andUniverseLayer.
The main loop runs in phases: input/platform update, project update, transform graph calculation, fixed update, scene update, render preparation, late update, render execution, and window presentation. Editor play mode clones the start scene for runtime simulation, then restores the project scene when playback stops.
ECS and Scene Model
EvoEngine uses two complementary component types:
| Component type | Use for |
|---|---|
| Data components | Plain standard-layout structs stored by archetype/chunk. Use them for high-volume simulation and parallel Scene::ForEach iteration. |
| Private components | Object-style components with lifecycle hooks, serialization, asset references, editor inspection, and per-entity behavior. |
Scenes own entity metadata, hierarchy, data component storage, private component storage, systems, environment state, and the main camera reference. Scene APIs cover entity creation/destruction, parenting, enable/static/name state, data component access, private component access, systems, queries, cloning, serialization, and prefab conversion.
Use data components when memory layout and parallel iteration matter most. Use private components when the feature needs editor UI, lifecycle methods, polymorphism, serialized state, or asset references.
Assets, Projects, and Serialization
Assets are handle-based and are managed by the asset, file, and project managers. Projects use .eveproj files and asset sidecars such as .evefilemeta and .evefoldermeta. Asset references serialize by handle and type name, then resolve through the asset manager.
Persistent engine types must be registered with the serialization system. New persistent types usually need:
- a type registration such as
AssetRegistration,PrivateComponentRegistration,DataComponentRegistration, or system registration SerializeandDeserializeOnInspectwhen editor editing is usefulCollectAssetRefandRelinkwhen the type storesAssetRef,EntityRef, or component/entity handles
Rendering
The SDK renderer is Vulkan-based and centered on RenderLayer. The renderer includes deferred and forward paths, shadow maps, PBR materials, environment lighting, skyboxes, render textures, editor cameras, gizmos, mesh/skinned/instanced/particle/strand draw paths, and optional meshlet, indirect, and ray tracing support where available.
Scene components describe rendering intent. Render instance storage converts scene state into GPU-friendly material, instance, camera, light, and environment buffers. Geometry and texture storage keep mesh and texture resources globally available to render passes.
Services can extend rendering through RenderLayer callbacks for shadow maps, deferred rendering, forward rendering, and custom render instance registration.
Jobs, Input, and Time
The SDK job system supports scheduled and immediate parallel work. ECS iteration helpers use jobs to process chunks in parallel. Input is routed from GLFW callbacks through the engine input system and layer event hooks. Timing utilities track frame delta time, fixed timestep state, and update counters.
Applications
| Target | Purpose |
|---|---|
DemoApp | General renderer/framework demo with multiple Service registrations. |
EcoSysLabApp | Interactive digital forestry and ecosystem workflow. |
DigitalAgricultureApp | Interactive sorghum and agriculture workflow. |
LogGradingApp | Log grading workflow; LogGrading and LogScanning features are supplied by runtime packages. |
TreeDataGeneratorApp | Batch-oriented tree dataset generation. |
SorghumDataGeneratorApp | Batch-oriented sorghum dataset generation. |
EmptyApp | Minimal SDK app with render/window/editor layers for quick experiments. |
Python Bindings
PythonBinding builds pybind11 modules for automation:
- core Python scripts and bindings that do not depend on runtime package C++ APIs
Package-specific Python C++ APIs for EcoSysLab and DigitalAgriculture are currently disabled while those domains move to runtime packages. Example scripts live in PythonBinding; package-level Python APIs should be added through a dedicated dynamic package interface later.
Services and Runtime Packages
EvoEngine now separates two extension models:
| Extension type | Folder | Build/runtime model | Use for |
|---|---|---|---|
| Service | EvoEngine_Services/<Name> | Static library selected by CMake options such as EVOENGINE_ENABLE_CudaModule_SERVICE | Build-time modules that apps or packages link against directly. |
| Runtime package | EvoEngine_Packages/<Name> | DLL/shared library selected by CMake options such as EVOENGINE_ENABLE_<Name>_PACKAGE and loaded from a Packages runtime folder | Domain features that can be rebuilt, loaded, unloaded, or reloaded independently from the app. |
When EVOENGINE_ENABLE_RUNTIME_PACKAGES is ON, the SDK builds as a shared library so apps, services, Python bindings, and runtime packages share the same runtime registries. Runtime packages export EvoEnginePackageGetDescriptor, EvoEnginePackageLoad, and EvoEnginePackageUnload. Packages may also export EvoEnginePackageRegisterTypes so RTTI/reflection types are registered before the normal load callback runs. A package can register private components, assets, data components, systems, and layers through PackageRegistrar.
Build Requirements
Clone with submodules:
git clone --recursive https://github.com/edisonlee0212/EvoEngine.git
cd EvoEngine
If the repository was cloned without submodules:
git submodule update --init --recursive
Windows requirements:
- Visual Studio 2019 or 2022 with Desktop development with C++
- CMake and Ninja
- Vulkan SDK
- vcpkg path recorded in
%LOCALAPPDATA%\vcpkg\vcpkg.path.txt - a Visual Studio developer command prompt
Windows build:
build.cmd Release
Linux requirements:
clang-14cmakeninja-buildlibwayland-devlibxkbcommon-devxorg-dev- Vulkan SDK from LunarG
- Python development headers, currently expected around Python 3.12 for the provided setup
Linux build:
bash build.sh Release
Useful script options:
--clean
--verbose
--no-test
Debug
Release
Build outputs are generated under out/build/<platform>-<config>/. App binaries are produced under the EvoEngine_App build directory and Python modules are produced under the PythonBinding build directory. Runtime packages are copied under the app runtime Packages folder. Post-build steps still copy engine resources, Service resources, runtime libraries (.dll on Windows, .so on Linux), PDBs when available, runtime packages, and imgui.ini beside build-tree binaries for fast local development.
CMake install provides a cleaner runtime deployment tree:
out/install/vs2026-x64/bin/
out/install/vs2026-x64/bin/Packages/
out/install/vs2026-x64/python/
The bin folder contains installed app executables plus their runtime libraries, PDBs when available, and resources. Runtime package libraries install under bin/Packages. The python folder contains installed Python extension modules, Python scripts, and the same runtime library/resource payload needed for imports and scripted workflows.
VSCode Build
VSCode on Windows should use CMakePresets.json through the CMake Tools extension. The preset file intentionally keeps only the two install build presets used for normal Visual Studio development:
install-vs2026-x64-Debuginstall-vs2026-x64-RelWithDebInfo
Select the vs2026-x64 configure preset, then choose one of those install build presets. They build the selected configuration and deploy the runtime payload to out/install/vs2026-x64.
The Visual Studio generator writes app executables to:
out/build/vs2026-x64/EvoEngine_App/<Config>/
Runtime package libraries are copied to the matching app Packages directory, for example:
out/build/vs2026-x64/EvoEngine_App/RelWithDebInfo/Packages/
From a terminal, use:
cmake --preset vs2026-x64
cmake --build --preset install-vs2026-x64-RelWithDebInfo
After install, app executables are under:
out/install/vs2026-x64/bin/
Runtime package libraries are under:
out/install/vs2026-x64/bin/Packages/
Python bindings and scripts are under:
out/install/vs2026-x64/python/
If CMake is configured with a Visual Studio generator instead, it will create .sln and .vcxproj files. Those files are project files, not final executables. To produce .exe files from that generator, the generated solution still needs to be built with Visual Studio, MSBuild, or cmake --build <build-dir> --config Debug.
On Linux, configure directly with CMake and install to a separate tree:
cmake -S . -B out/build/linux-RelWithDebInfo -DCMAKE_BUILD_TYPE=RelWithDebInfo -DCMAKE_INSTALL_PREFIX=out/install/linux-RelWithDebInfo
cmake --build out/build/linux-RelWithDebInfo
cmake --install out/build/linux-RelWithDebInfo
Linux runtime libraries are deployed as .so files beside the installed apps and Python modules.
SDK Extension Guide
When adding new work:
- Add a private component when behavior belongs to an entity and needs lifecycle hooks, editor UI, serialization, or asset references.
- Add data components when the feature is high-volume and benefits from chunked ECS iteration.
- Add a system when behavior should run over a scene independently of one component instance.
- Add an asset when data should be reusable, referenceable, and stored in projects.
- Add a layer when behavior is global to the application or needs top-level UI/render/input hooks.
- Add a service when the feature is a build-time dependency and should remain outside the SDK.
- Add a runtime package when the feature should be loaded, unloaded, or rebuilt independently from a running app.
- Add a Python binding when a workflow should run from scripts.
Runtime Package Development
Runtime packages live under EvoEngine_Packages. The package CMake entry scans package folders automatically, reads optional metadata from PackageInfo.cmake, creates an EVOENGINE_ENABLE_<Name>_PACKAGE option, and builds a shared library target named <Name>Package by default. Disable individual package targets with EVOENGINE_ENABLE_<Name>_PACKAGE=OFF when a build should skip them.
Apps do not load runtime packages by default. Set ApplicationInitializationSettings::enable_runtime_packages = true and add names to ApplicationInitializationSettings::startup_runtime_packages, or call PackageManager::Load/LoadAll from runtime/editor tooling when a workflow needs additional package functionality.
Package dependencies are declared with EVOENGINE_PACKAGE_DEPENDS in PackageInfo.cmake. CMake builds dependencies first and emits a sidecar .evepackage manifest beside each package binary so the runtime can discover and load dependencies before opening a package DLL/shared library.
A package must export the descriptor/load/unload entrypoints. Packages that own RTTI/reflection types should also export EvoEnginePackageRegisterTypes:
EvoEnginePackageGetDescriptor
EvoEnginePackageRegisterTypes
EvoEnginePackageLoad
EvoEnginePackageUnload
Use EvoEnginePackageRegisterTypes and the provided PackageRegistrar to register package-owned RTTI/reflection types:
registrar.RegisterPrivateComponent<MyComponent>("MyComponent");
registrar.RegisterAsset<MyAsset>("MyAsset", {".myasset"});
registrar.RegisterDataComponent<MyData>("MyData");
registrar.RegisterSystem<MySystem>("MySystem");
registrar.RegisterLayer<MyLayer>("My Layer");
Package unloading is guarded. Reload/unload is refused while the app is playing or stepping, and it is also refused while package-owned private component instances or other package-created objects still exist. On Windows, packages are loaded from a shadow copy so the original DLL can usually be rebuilt while the app process remains open.
License
This repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International license. See LICENSE for the full text.
2. Service Documentation
Service and runtime package documentation is split into separate Markdown files so each module can grow independently without turning the README into a wall of details.
| Service | Status | Documentation |
|---|---|---|
| CudaModule | Present but disabled by default | EvoEngine_Services/CudaModule/README.md |
| PhysXPhysics | Present but disabled in its CMake file | EvoEngine_Services/PhysXPhysics/README.md |
| Runtime Package | Status | Documentation |
|---|---|---|
| Universe | Built by default | EvoEngine_Packages/Universe/README.md |
| BillboardClouds | Built by default | EvoEngine_Packages/BillboardClouds/README.md |
| EcoSysLab | Built by default; depends on BillboardClouds | EvoEngine_Packages/EcoSysLab/README.md |
| DigitalAgriculture | Built by default; depends on EcoSysLab | EvoEngine_Packages/DigitalAgriculture/README.md |
| DatasetGeneration | Built by default; depends on EcoSysLab and DigitalAgriculture | EvoEngine_Packages/DatasetGeneration/README.md |
| TextureBaking | Built by default on Windows | EvoEngine_Packages/TextureBaking/README.md |
| MeshRepair | Built by default on Windows | EvoEngine_Packages/MeshRepair/README.md |
| Gpr | Built by default on Windows | EvoEngine_Packages/Gpr/README.md |
| LogGrading | Built by default on Windows; requires EcoSysLab | EvoEngine_Packages/LogGrading/README.md |
| LogScanning | Built by default on Windows; requires EcoSysLab and Pinchot | EvoEngine_Packages/LogScanning/README.md |
The Service index is also available at EvoEngine_Services/README.md. Runtime package documentation is available at EvoEngine_Packages/README.md.
Service Build Model
Services are registered from EvoEngine_Services/CMakeLists.txt. The registration macro creates an EVOENGINE_ENABLE_<ServiceName>_SERVICE option, adds the Service subdirectory, and appends the Service target, include paths, compile definitions, precompiled headers, copied resources, and runtime libraries to the shared EvoEngine build variables.
The common pattern is:
- Service source lives under
EvoEngine_Services/<ServiceName>/includeandsrc - Service target is a static library named
<ServiceName>Service - Service compile definitions are uppercase module names such as
CUDA_MODULE_SERVICEorPHYSX_PHYSICS_SERVICE - Service resources may be copied from an
Internalsfolder - app targets link against the enabled Service list
For example, configure with -DEVOENGINE_ENABLE_CudaModule_SERVICE=OFF to disable a build-time service, or -DEVOENGINE_ENABLE_EcoSysLab_PACKAGE=OFF to skip a runtime package and its dependents.
Demo Projects and Visual Results
Demo projects and visual assets live under Resources.
| Area | Preview |
|---|---|
| Rasterized rendering | ![]() |
| Ray tracing | ![]() |
| Planet terrain | ![]() |
| Star clusters | ![]() |
| Tree framework | ![]() |
| Tree fracture | ![]() |
| Strand visualization | ![]() |
| Sorghum model | ![]() |
| Sorghum point cloud | ![]() |
| Sorghum environment lighting | ![]() |
| Illumination estimation | ![]() |
Related Publications
EvoEngine supports research workflows used in digital forestry and digital agriculture. Related work includes:
- Learning to Reconstruct Botanical Trees from Single Images, SIGGRAPH Asia 2021
- Rhizomorph: The Coordinated Function of Shoots and Roots, SIGGRAPH 2023
- DeepTree: Modeling Trees with Situated Latents, TVCG 2023
- Latent L-systems: Transformer-based Tree Generator, SIGGRAPH 2024
- Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors, ECCV 2024
- Interactive Invigoration: Volumetric Modeling of Trees with Strands, SIGGRAPH 2024
- TreeStructor: Forest Reconstruction With Neural Ranking, TGRS 2025
- Stressful Tree Modeling: Breaking Branches with Strands, SIGGRAPH 2025
- 3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves, PeerJ
- Sorghum segmentation and leaf counting using in silico trained deep neural model, The Plant Phenome Journal
- PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics, Computers and Electronics in Agriculture
- Woodstock









