Samenvatting

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 (??) and image resolutions reveals optimal trade-offs: ?? 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 performancesensitive
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.