Utilizing JIT Python runtime and parameter optimization for CPU-based Gaussian Splatting thumbnailer

Gaussian Splatting has emerged as a powerful technique for high-fidelity 3D scene representation, yet its computational demands hinder rapid visualization, particularly on CPU-based systems. This paper introduces a lightweight method for efficient thumbnail generation from Gaussian splatting data, leveraging Just-in-Time (JIT) compilation in Python to optimize performance-critical operations. By integrating the Numba JIT compiler and strategically simplifying parameters, by omitting rotation data and approximating Gaussians as spheres, we achieve significant speed improvements while maintaining visual eligibility. Systematic experimentation with Gaussian splat sizes ( σ ) and image resolutions reveals optimal trade-offs: σ values of 0.4–0.5 balance detail and speed, allowing 720p thumbnail generation in 1.8 s. JIT compilation reduces execution time by 156×compared to pure Python (from 336 to 2.33 s), transforming Python into a viable tool for performance-sensitive tasks. The CPU-focused design ensures portability across devices, addressing resource-constrained scenarios like criminal investigations or field operations. Although limitations in Python’s inherent performance ceiling persist, this work demonstrates the potential of JIT-driven optimizations for lightweight 3D rendering, offering a pragmatic solution for rapid previews without GPU dependency. Future directions include migration to compiled languages and adaptive parameter tuning to further enhance scalability and real-time applicability. • Developed a CPU-only pipeline for fast thumbnail extraction from Gaussian Splatting. • Achieved 156×speedup using JIT compilation with Python and Numba optimization. • Found optimal trade-off at σ = 0 . 4 –0.5 and 720p resolution for clarity and speed. • Used rotation-free sphere splats to reduce compute cost while preserving visuals. • Enables real-time thumbnailing in low-resource, GPU-less forensic environments.