Neural PBR Textures: Compression and Rendering Acceleration with Autoencoders
Neural compression of PBR textures (albedo, normal, roughness, metallic) by 5–10x without visible quality loss. Faster scene loading and optimized rendering for games, VR/AR, and 3D graphics.
Tasks
- Develop a neural autoencoder for compressing PBR textures (albedo, normal, roughness, metallic).
- Ensure visually imperceptible losses during texture reconstruction.
- Optimize texture storage format for fast reading and rendering.
- Ensure compatibility with game engines and renderers.
About the Project
Physically Based Rendering (PBR) is the standard in modern 3D graphics, but highly detailed textures (albedo, normals, roughness, metallic) consume significant memory and slow down rendering.
Our project offers an innovative approach to storing and using PBR textures: using autoencoders, we transform traditional textures into compact neural representations.
Results
Key Benefits
- Compression – neural network reconstructs textures with high accuracy
- Fast Loading – reduced memory and disk space usage
- Optimized Rendering – faster performance in real-time (games, VR/AR)
- Support for Standard PBR Maps (albedo, normal, roughness, metallic)
- Preservation of physical material accuracy
Applications
- Game Engines (Unreal Engine, Unity)
- reduced weight of texture atlases
- Film and Animation
- accelerated production rendering
- Mobile and AR/VR Applications
- memory savings and fast loading
- 3D Visualization
- efficient handling of large scenes
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