Exploring Gaussian Splats for Museum Applications

I recently created a 3D reconstruction using Gaussian Splatting, an emerging method for representing 3D scenes with remarkable realism and efficiency. Unlike traditional photogrammetry, which builds surfaces from meshes and textures derived from many overlapping photos, Gaussian splats represent scenes as dense fields of tiny "points of light" with color, opacity, and spatial variance. The result is smoother lighting, faster rendering, and a more immersive visual experience without the heavy geometry of mesh-based models.

Traditional photogrammetry creates 3D models by reconstructing surfaces—building meshes with vertices, edges, and faces, then applying textures derived from photographs. This approach works well but can produce visible seams, require significant processing power, and result in large file sizes.

Gaussian Splatting takes a fundamentally different approach. Instead of surfaces, it represents scenes as collections of 3D Gaussian primitives—essentially tiny, oriented "splats" that each carry color, opacity, and spatial information. These splats are optimized to represent the scene efficiently, creating smooth, photorealistic renderings that can be displayed in real-time.

I plan to continue experimenting with this technology to understand its potential in the museum and cultural heritage space—especially for digitizing artifacts, exhibition spaces, and outdoor monuments. The advantages could include rapid visualization, real-time interactivity, and lower data preparation overhead compared to traditional scanning workflows.

As the technology matures, it may complement photogrammetry rather than replace it—offering curators, researchers, and the public new ways to experience cultural heritage through lightweight, high-fidelity 3D presentations. Each method has its strengths:

The choice between methods will depend on the specific goals of each project—whether you need a printable model, a web-based interactive experience, or both.

This technology is still evolving, and I'm excited to explore its applications in our work. The combination of visual quality, rendering speed, and efficient data representation makes it particularly promising for creating engaging digital experiences that bring museum collections to life online.

As we continue to experiment and refine our workflows, Gaussian Splatting may become another valuable tool in our digital preservation toolkit—one that complements rather than replaces existing methods, giving museums and cultural institutions more options for how they present and preserve their collections.