3D Gaussian Splatting vs. Photogrammetry: Real-Time Spatial Rendering Guide

Spatial computing and 3D scan visualization are undergoing a major paradigm shift. While traditional photogrammetry converts point clouds into textured polygonal meshes, 3D Gaussian Splatting represents scene radiance fields directly through continuous 3D Gaussians, offering unprecedented visual fidelity at real-time frame rates.

1. Polygonal Mesh Reconstruction vs. Radiance Field Rasterization

Traditional photogrammetry relies on dense image matching, depth map generation, Poisson surface reconstruction, and UV texture baking. This pipeline yields hard geometry that works seamlessly with standard game engines but struggles with view-dependent specular reflections, fine foliage, and semi-transparent surfaces. Gaussian Splatting bypasses polygon topology entirely, rasterizing millions of parameterized 3D Gaussians directly into screen-space tiles using CUDA or WebGPU.

2. VRAM Consumption, Memory Footprint, and WebGL Bottlenecks

While 3D Gaussian Splatting achieves photorealistic reflections without complex ray tracing, the memory bandwidth required to sort and blend millions of semi-transparent Gaussians per frame is substantial. Optimizing `.splat` or `.ply` files for web browsers requires spherical harmonic pruning, vector quantization, and dynamic level-of-detail (LOD) distance sorting to avoid choking WebGL pipelines on mobile devices.

3. Hybrid Pipelines: Converting Point Clouds for Blender and Unreal Engine

For AEC applications, BIM workflows, and collision-based game assets, raw radiance fields must still interact with physical environments. Modern pipelines increasingly employ hybrid approaches: utilizing 3D Gaussian Splatting for ultra-high-fidelity ambient backgrounds, alongside Poisson surface extraction or quad retopology in Blender to convert core structural scanned components into collision-ready meshes.