orillusion/orillusion

Orillusion is a pure Web3D rendering engine which is fully developed based on the WebGPU standard.

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Summary Information

Updated 9 minutes ago
Added to GitGenius on September 13th, 2026
Created on March 14th, 2023
Open Issues & Pull Requests: 48 (+0)
GitHub issues: Enabled
Number of forks: 601
Total Stargazers: 5,213 (+0)
Total Subscribers: 341 (+0)

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Issue Activity (beta)

Open issues: 14
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 492 days
Stale 30+ days: 14
Stale 90+ days: 8

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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  • bug (31)
  • feature (19)
  • discussion (1)
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Detailed Description

Orillusion is a Web3D rendering engine built entirely on the WebGPU standard for browser-based 3D graphics.

The engine addresses the need for desktop-level 3D rendering capabilities in web browsers by leveraging WebGPU, a modern graphics API standard. It enables complex scene rendering directly in the browser without requiring plugins or external dependencies. The architecture is built from the ground up around WebGPU rather than adapting older graphics APIs, allowing it to take full advantage of modern GPU capabilities for advanced visual effects.

Developers considering adoption should be aware that the project is in beta and explicitly not recommended for commercial applications. The tool suits experimental projects, research, and early-stage development where cutting-edge WebGPU support is desired. It works with standard front-end build tools like Vite and Webpack, and can also be loaded via CDN using native script tags for simpler integration scenarios. The project positions itself as targeting front-end developers interested in staying current with the latest web technologies beyond traditional web design.

The project maintains active contribution guidelines and actively solicits sample implementations from the open-source community. The maintainers emphasize building a dedicated technical community around WebGPU development and regularly integrate excellent samples provided by contributors into the repository.