Research on GPU-based parallel computing for global illumination algorithms in indoor virtual reality scenes

Artificial intelligence-driven intelligent algorithms have become key technologies for addressing real-time lighting solution bottlenecks in virtual reality rendering. Background: Traditional CPU-based global illumination calculations suffer from high path intersection overhead and slow energy convergence in complex indoor scenes, making it difficult to meet VR's high frame rate and low latency requirements. Objective: To leverage the high throughput characteristics of GPU parallel architectures to construct an efficient light energy transfer model, achieving real-time, stable indoor global illumination rendering. Method: We propose a GPU-based parallel lighting solution pipeline, designing a thread-bundle scheduling model. Memory access efficiency is optimized through BVH hierarchical traversal and local caching mechanisms, while multi-sample fusion and path flattening techniques enhance lighting consistency. Results: Algorithm performance validated on NVIDIA RTX 4090 platform achieves: average frame time reduced to 11.6 ms, lighting convergence steps decreased by 68.3%, VR frame rate increased to 94 FPS, and noise residual reduced by 73.4%—significantly outperforming CPU-based solutions. Conclusion: This GPU-parallel global illumination algorithm achieves real-time rendering while maintaining physical lighting accuracy. It provides a scalable computational framework for high-quality visualization in virtual reality scenarios and lays the technical foundation for future multi-GPU coordination and adaptive optimization of dynamic light fields.