Project Elements: A computational entity-component-system in a scene-graph pythonic framework, for a neural, geometric computer graphics curriculum

TL;DR

The Elements project introduces a novel Python-based framework combining ECS and scene-graph concepts to enhance computer graphics education, bridging traditional curricula with modern game engine techniques and neural visualization.

Contribution

It is the first to integrate ECS with scene-graph in a Python framework for CG education, enabling detailed teaching of advanced concepts and neural network representations.

Findings

Supports heterogeneous directed acyclic graphs and depth-first traversals

Enables animation, skinning, and shader-based rendering in an educational setting

Facilitates representation of CG concepts as graph neural networks

Abstract

We present the Elements project, a lightweight, open-source, computational science and computer graphics (CG) framework, tailored for educational needs, that offers, for the first time, the advantages of an Entity-Component-System (ECS) along with the rapid prototyping convenience of a Scenegraph-based pythonic framework. This novelty allows advances in the teaching of CG: from heterogeneous directed acyclic graphs and depth-first traversals, to animation, skinning, geometric algebra and shader-based components rendered via unique systems all the way to their representation as graph neural networks for 3D scientific visualization. Taking advantage of the unique ECS in a a Scenegraph underlying system, this project aims to bridge CG curricula and modern game engines (MGEs), that are based on the same approach but often present these notions in a black-box approach. It is designed to actively utilize software design patterns, under an extensible open-source approach. Although Elements provides a modern (i.e., shader-based as opposed to fixed-function OpenGL), simple to program approach with Jupyter notebooks and unit-tests, its CG pipeline is not black-box, exposing for teaching for the first time unique challenging scientific, visual and neural computing concepts.