Neural Dynamic GI: Random-Access Neural Compression for Temporal Lightmaps in Dynamic Lighting Environments
Summary: arXiv:2604.12625v1 Announce Type: cross
Abstract
High-quality global illumination (GI) in real-time rendering is commonly achieved using precomputed lighting techniques, with lightmap as the standard choice. To support GI for static objects in dynamic lighting environments, multiple lightmaps at different lighting conditions need to be precomputed, which incurs substantial storage and memory overhead.
Introduction
To address the challenges posed by dynamic lighting environments, researchers have been exploring innovative techniques to enhance the efficiency of global illumination without compromising quality. Traditional methods that rely on precomputed lightmaps face significant limitations, particularly in terms of storage and memory usage.
Proposed Method: Neural Dynamic GI (NDGI)
In response to these challenges, we propose Neural Dynamic GI (NDGI), a novel compression technique specifically designed for temporal lightmap sets. Our method leverages multi-dimensional feature maps and lightweight neural networks to effectively integrate temporal information. This innovative approach eliminates the need to store multiple lightmap sets explicitly, resulting in a significant reduction in storage size.
Key Features of NDGI
- Multi-Dimensional Feature Maps: NDGI utilizes advanced multi-dimensional feature maps that allow for a more compact representation of lightmap data.
- Lightweight Neural Networks: The integration of lightweight neural networks ensures that the computational overhead remains low while still providing high-quality output.
- Block Compression Simulation: We introduce a block compression (BC) simulation strategy during the training process, which further enhances the compression ratio of the final generated feature maps.
- Virtual Texturing System: To facilitate efficient real-time decompression, we incorporate a virtual texturing (VT) system that works seamlessly with our neural representation.
Performance Comparison
Compared to prior methods, our approach achieves high-quality dynamic GI while maintaining remarkably low storage and memory requirements. The modest real-time decompression overhead further enhances the practicality of NDGI for real-time applications.
Future Work and Dataset Release
To facilitate further research in this direction, we are committed to releasing our temporal lightmap dataset, which has been precomputed in multiple scenes featuring diverse temporal variations. This dataset will serve as a valuable resource for researchers and developers aiming to explore new frontiers in global illumination techniques.
Conclusion
Neural Dynamic GI represents a significant advancement in the field of global illumination for dynamic lighting environments. By efficiently compressing temporal lightmap data, our approach not only enhances rendering quality but also addresses critical storage and performance challenges faced by traditional methods.