Gaussian Splatting vs NeRFs

Rendering Performance: Orders of Magnitude Apart

The most striking difference between Gaussian Splatting and NeRF is rendering speed. Gaussian Splatting's rasterization-based approach — projecting 3D Gaussians onto the image plane and alpha-compositing — maps directly to GPU hardware pipelines. Recent advances like FlashGS (CVPR 2025) have pushed this to stable 4K real-time rendering, while IBM Research's LangSplatV2 demonstrates 476 FPS for high-dimensional feature splatting. This is not incremental improvement; it represents two to three orders of magnitude faster rendering than fully implicit NeRF methods.

NeRF's ray-marching approach — casting rays through every pixel and evaluating a neural network at hundreds of sample points per ray — is inherently more compute-intensive. Optimized variants like Instant-NGP brought dramatic speedups through multi-resolution hash encodings, but even the fastest NeRF implementations typically top out at 5–30 FPS. For applications in virtual reality and augmented reality where 90+ FPS is a hard requirement, this gap is decisive.

Visual Quality and Scene Fidelity

Both techniques produce photorealistic novel views, but they excel in different areas. Gaussian Splatting handles discontinuities well — sharp edges, thin structures like fences and hair, and semi-transparent materials render cleanly because each Gaussian is an independent primitive. NeRF's continuous volumetric representation produces smoother surfaces and more physically accurate view-dependent effects like specular highlights, refractions, and subtle color shifts with viewing angle.

For most practical applications, the quality difference is negligible. Where it matters is in close-up inspection of smooth, glossy surfaces (NeRF advantage) versus complex, detailed environments with many fine structures (Gaussian Splatting advantage). Recent hybrid approaches aim to combine the best of both, using neural networks to refine Gaussian attributes for improved view-dependent accuracy.

Practical Deployment and Standardization

Gaussian Splatting has pulled dramatically ahead in real-world deployment. The August 2025 announcement that Khronos and OGC would add Gaussian Splats to the glTF ecosystem — with Niantic's open-source SPZ as the compact container format — was a watershed moment. Cesium's March 2026 platform releases marked one of the first major implementations of the KHR_gaussian_splatting standard. This standardization trajectory mirrors how JPEG became universal for 2D images.

NeRF lacks any comparable standardization. Each implementation uses its own format, making interoperability difficult. For organizations building content pipelines around 3D content creation and digital twins, Gaussian Splatting's ecosystem maturity is a significant practical advantage.

Compression and Streaming at Scale

Raw Gaussian Splat files can be large — hundreds of megabytes for complex scenes with millions of Gaussians. However, 2025 saw a wave of compression breakthroughs. Niantic's SPZ format achieves roughly 90% size reduction, while academic methods like HAC++ demonstrate up to 100× compression through learned factorization and hierarchical representations. Voyager's streaming approach transmits only newly visible splats, achieving over 100× less data transfer and up to 8.9× client speedups for city-scale mobile experiences.

NeRF's implicit representation is inherently compact — a trained network might be just 5–50 MB — but this advantage has narrowed considerably as Gaussian Splatting compression matures. More importantly, NeRF requires neural network inference on the client device, while compressed Gaussian Splats can be rendered through standard rasterization pipelines, making them far more practical for WebGPU-based browser delivery.

Editability and Content Creation Workflows

Gaussian Splatting's explicit representation — where every element is a discrete, addressable primitive — makes scene editing intuitive. Artists can select, move, delete, or recolor individual Gaussians or groups of them. Scenes can be composed by combining splat clouds from different captures. This maps well to existing 3D content creation workflows and tools.

NeRF scenes are encoded in neural network weights, making direct editing extremely difficult. Changing part of a scene typically requires retraining or using specialized editing networks. While research has produced techniques for NeRF editing (object removal, style transfer, relighting), these are far less flexible than Gaussian Splatting's direct manipulation. For production environments in game development and film production, this editability gap is a major factor.

The Convergence: Hybrid Approaches

The most promising direction in 2025–2026 research is combining NeRF and Gaussian Splatting. Hybrid models use neural networks to optimize or refine Gaussian attributes, preserving NeRF's quality for view-dependent effects while maintaining Gaussian Splatting's rendering speed. Other approaches use NeRF as an initialization or supervision signal for Gaussian Splat generation.

This convergence suggests that the future may not be a binary choice. Instead, content pipelines may use NeRF-like neural optimization during capture and processing, then export to Gaussian Splat representations for real-time delivery — combining the strengths of both paradigms across different stages of the 3D content lifecycle.