Scene Graphs vs. Neural Radiance: What Engineers Actually Need

Neural Radiance Fields (NeRFs) and their descendants have captured the imagination of the 3D vision community. The renders are stunning. But when engineers need to make decisions about physical infrastructure, beautiful renders aren't enough.

What NeRFs Give You

NeRFs are fundamentally view synthesis models. They learn to render novel views of a scene from a set of input images. The results look photorealistic, but the underlying representation is an opaque neural network — not a structured model you can query or reason about.

You can ask a NeRF: "What does this scene look like from angle X?" You cannot ask: "How many defects are on this bridge?" or "What material is that beam made of?"

What Scene Graphs Give You

A scene graph is a structured, queryable representation of the physical world. Every object is a node with properties. Every relationship is an edge with semantics.

# You can't do this with a NeRF
defects = scene.query(
  type="structural_defect",
  material="reinforced_concrete",
  severity="critical"
)

for defect in defects:
    print(f"{defect.object_id}: {defect.type} at {defect.position}")
    print(f"  Material: {defect.material}")
    print(f"  Confidence: {defect.confidence}")

The Engineering Perspective

Engineers don't need novel view synthesis. They need:

1. Measurements — What are the dimensions of this structure?
2. Classification — What material is each component?
3. Change detection — What's different since the last inspection?
4. Simulation readiness — Can I run physics on this model?

Scene graphs deliver all four. NeRFs deliver none.

The question isn't "which produces better-looking output?" It's "which enables better decisions?"

Why Not Both?

At Percept, we actually use neural techniques under the hood for reconstruction. But the output is always a structured scene graph — not an opaque neural field. We get the best of both: neural methods for processing, structured representations for reasoning.