For decades, 3D graphics has relied on a familiar toolbox: polygon meshes, textures, and carefully authored geometry. These techniques are powerful, but they are also labor-intensive. Creating high-quality 3D assets typically requires skilled artists, specialized software, and significant time.

In parallel, research introduced alternatives such as point clouds, voxels, and more recently, Neural Radiance Fields (NeRFs). Each brought improvements, but also trade-offs. Point clouds are sparse and visually brittle. Voxels are memory-hungry. NeRFs produce stunning visuals but are slow to render and difficult to integrate into real-time systems.

What many industries needed—but did not yet have—was a representation that combined:

• Fast capture from real-world imagery

• High visual fidelity

• Real-time rendering performance

• Compatibility with modern GPUs

3D Gaussian Splatting emerged as a response to that gap.

At its core, 3D Gaussian Splatting represents a scene not as triangles or surfaces, but as millions of tiny, soft volumetric elements, called Gaussians.

Each Gaussian can be thought of as a small, fuzzy particle in 3D space. Instead of being a single point, it has:

• A position in 3D space

• A size and orientation (how it spreads)

• Color and opacity information

When rendered together, these Gaussians overlap and blend, forming a continuous, photorealistic scene.

Unlike traditional geometry, there is no explicit surface. Unlike NeRFs, there is no neural network being queried at render time. The scene is simply a collection of optimized primitives that GPUs are very good at drawing.

The creation process starts with something familiar: images or video of a real scene, often captured from multiple viewpoints.

From there, an optimization process estimates where Gaussians should be placed, how large they should be, and what color and opacity they should carry in order to reproduce the captured views as accurately as possible.

Rather than manually modeling geometry, the system learns a representation that best explains the visual data. The output is not a neural model, but a dataset: a structured collection of Gaussians.

This distinction is important. Once trained, a Gaussian splat scene is static data. It can be stored, transmitted, loaded, and rendered directly—without inference or heavy computation.

One of the most striking aspects of Gaussian Splatting is its performance. Scenes can often be rendered at real-time frame rates, even with millions of Gaussians.

This is largely because:

• Rendering is reduced to drawing and blending simple primitives

• There is no ray marching or neural evaluation

• The representation maps well to GPU pipelines

In practice, this means interactive viewing, smooth navigation, and immediate visual feedback—qualities that were previously difficult to achieve with comparable visual quality.

This performance advantage is a major reason Gaussian Splatting has been rapidly adopted in research demos, XR experiments, and early production pipelines.

Like any representation, Gaussian Splatting is not a silver bullet.

Strengths include:

• Extremely fast capture from real-world imagery

• High visual realism

• Real-time rendering capability

Trade-offs include:

• Larger file sizes compared to meshes

• Limited support for traditional editing workflows

• Ambiguity around ownership and provenance

That last point is subtle but important. When assets are easy to capture, duplicate, and redistribute, questions of authorship and control become harder to answer.

Gaussian Splatting is already finding its way into:

• Digital twins and virtual environments

• Cultural heritage and archival scanning

• Game and XR prototyping

• Remote visualization of real-world spaces

As tooling matures, these splat-based assets are increasingly shared, reused, and remixed across teams and platforms.

And that leads to a new challenge—not one of rendering, but of trust.

When a 3D asset can be captured in minutes and shared as easily as a video file, traditional assumptions about ownership break down.

A few questions to consider:

• Who created it?

• Who owns it?

• How can that be proven once it leaves its original context?

To answer those questions, we need protection mechanisms designed for this new kind of representation—not retrofitted from older ones.

That is where GuardSplat enters the picture.

Please see Part 3: Protecting Reality in 3D – Part 3: GuardSplat Explained