3D Environments & Scenarios for Physical AI

Generate controlled variation across edge case and long-tail scenarios.

We build structured 3D environments grounded in real-world scenarios, amplifying your existing datasets by expanding into the edge cases and long-tail situations they don’t cover. Every variable, including lighting, obstruction, weather, and agent behavior, can be systematically adjusted and combined. Every scene arrives fully annotated, consistently and precisely, with no manual labeling required.

Talk to our team →

One scenario, compounding variables

BASELINE

Industrial Environment

EDGE CASE

Industrial Environment + Obstruction

RARE EDGE CASE

Industrial Environment + Obstruction + Low Visibility

COMPOUND EDGE CASE

Industrial Environment + Obstruction + Low Visibility + Equipment Failure + Moving Agent

Built for

Robotics Autonomous Systems Computer Vision Embodied & Multimodal AI

The Problem

Real-world data is abundant. The scenarios you need aren’t.

Edge Cases Are Underrepresented

They’re not impossible to film. They just occur too infrequently to collect at the volume and variety models need.

Rare by definition

The Long Tail Is Expensive to Capture

Staging a safety-critical event with slight variations requires shutting down real environments and coordinating personnel. Teams can’t afford to do it systematically.

One setup. Dozens of rehearsals.

Static Datasets Can’t Fill the Gaps

When a model fails on something outside its training set, the only fix is more data, even as deployment keeps surfacing new failure modes.

The failure modes you haven’t seen yet matter most.

How It Works

Reconstruct in 3D. Control any variable. Export anywhere.

What’s controllable

Environment

sparse shelves pallet moved left corner obstruction

Visibility

darkness pallet moved left smoke / fog

Timing

delayed action near-miss timing unexpected movement

Agent Behavior

atypical pose new body type unexpected action

Motion

different speed collision course new trajectory

How we build it

Reconstruct in 3D

Real-world environments reconstructed as structured 3D scenes, with the same spatial logic and physical scale. A grounded starting point you can extend in any direction.

Edit any variable

Camera, lighting, objects, agents, or any other parameter, all edited non-destructively. The base scene stays intact.

Deliver labeled

Exports to Unreal Engine, Isaac Sim, ROS 2, or your pipeline with native labels included. No re-annotation needed.

Unreal Engine Isaac Sim ROS 2 Omniverse

The Differentiator

Don’t just generate more data.
Generate the data you’re missing.

Scenario	Clear	Rain	Fog	Low light	Night
Normal traffic	✓	✓	✓	✓	✓
Moving agent	✓	✓	✓	✓	✓
Obstruction	✓	✓	✓	✓	✓
Equipment failure	✓	✓	✓	✓	✓
Unexpected behavior	✓	✓	✓	✓	✓
Occluded interaction	✓	✓	✓	✓	✓

Systematic Coverage

Control independently. Change one variable, hold the rest, and isolate exactly what your model is missing.

Sweep programmatically. Define a range, generate every combination automatically.

Fill specific holes. Target the exact gaps in your coverage matrix, not more of what you already have.

Automatic Annotation

Every scenario arrives fully labeled.

Labels come from scene geometry, not image inference. Every variation arrives annotated automatically.

Example Scene Output

Object IDs Lighting Weather Depth Obstruction Segmentation Hazards

{
  "scene_id": "wh_001_compound_edge_v3",
  "frame": 142,
  "labels": {
    "object_ids": [
      { "id": 7, "class": "forklift", "occluded": 0.34 },
      { "id": 19, "class": "moving_agent", "occluded": 0.71 }
    ],
    "lighting": { "condition": "low_artificial", "intensity": 0.42 },
    "obstruction": { "blocked_ids": [19] },
    "hazards": ["aisle_zone_A", "forklift_path_B"],
    "weather": { "fog_density": 0.28, "visibility_m": 14.5 }
  }
}

Every variant of the same scene gets this automatically, with no separate annotation pass.

The Comparison

Synthetic doesn’t automatically mean controlled.

Structured 3D is grounded in real-world environments and extends them. It amplifies what real-world capture provides, covering the variation that’s too rare, dangerous, or expensive to collect at scale.

Decision dimension	Real-World Capture	AI-Generated Synthetic	Structured 3D
Reproducibility	Limited	Prompt-dependent	Exact scene state
Physical Consistency	Observed reality	Probabilistic	Geometry-grounded
Labeling	Manual annotation	May require inference	Scene-native ground truth
Variation Control	Collect what occurs	Indirect	Edit any parameter
Cost to Resample	High / operational	Variable	Low after scene setup
Editability	Not editable	Hard to reproduce	Editable scene graph
Scale	Bound by capture	Bound by consistency	Programmatic coverage