Computer Graphics Engineers/Rendering Researchers: Professionals focused on real-time rendering, global illumination, and spatial acceleration structures (e.g., BVH, Hash Grids).
AI/LLM-Assisted Software Engineers: Practitioners interested in the "vibe coding" paradigm—using Large Language Models (LLMs) to accelerate rapid prototyping and architectural scaffolding in high-complexity domains.
Mobile Graphics Optimization Specialists: Developers familiar with the constraints of hardware-accelerated Vulkan/Metal APIs on mobile chipsets (e.g., Samsung Galaxy S26 Ultra/Snapdragon architecture).
Abstract
This technical report details a rapid prototyping experiment aimed at implementing Pascal Gautron’s "Real-Time Ray-Traced Ambient Occlusion of Complex Scenes using Spatial Hashing" via the LightweightVK framework. The project evaluates the efficacy of Claude (LLM) as a co-pilot for translating academic graphics papers into functional code. The author demonstrates that while AI significantly accelerates initial implementation—reducing a typical weekend-long task to a four-hour evening session—the process reveals clear limitations in AI's capacity for complex heuristic selection and cache invalidation optimization. The final implementation, optimized for mobile hardware, trades raw speed for visual fidelity, highlighting the recurring engineering gap between "proof-of-concept" and production-grade stability.
Summary: Implementation of Spatial Hashing for RT-AO
Objective: Implement spatial-hashing-based ray-traced ambient occlusion (RT-AO) to improve visual quality over baseline jittered stochastic noise on a mobile device (Samsung Galaxy S26 Ultra).
Workflow Integration (0:00–4:00 hours):
Phase 1 (Scaffolding): Claude was utilized to design the architecture, including necessary buffer layouts, shader modifications (GLSL/Slang), and the render loop structure.
Phase 2 (Debugging): Initial implementation required 10 minutes of refinement to resolve structural errors; a second iteration was required to correctly separate cached AO from real-time ray-traced AO.
Phase 3 (Heuristics): AI proved ineffective at optimizing hash table thrashing. Success was achieved only through manual human intervention, testing specific heuristics suggested by the developer.
Performance vs. Fidelity Trade-off: The naive spatial hashing implementation is ~1.5x slower than the naive jittered version but yields superior visual output.
Cache Invalidation Strategy:
The implementation uses an N-frame cache retirement strategy.
Empirical testing determined that a 2-frame lifespan provides the optimal balance between convergence and responsiveness to camera movement.
Future Optimization Pathways:
The developer identifies "invisible guard bands" around the camera frustum as the key to extending cache age. This allows temporal samples to be pre-warmed for geometry before it enters the active viewport.
Key Engineering Takeaway: The "vibe coding" approach is effective for rapid prototyping and exploring academic papers, but manual intervention remains mandatory for performance-critical heuristics, cache logic, and technical debt management.