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Scaniverse appCapture and reconstruct real places.PricingPlans for teams and enterprises.
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Where people and machines work in the real world.

Field operator at a refineryOil and gasIntelligent field operations. Aerial view of a coastal site, measured from drone imageryDefense and intelligenceMission rehearsal and GPS-denied operations. Robots in a real-to-sim workshopEmbodied AIReal-to-sim training for robots.
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The team, partners and resources behind Niantic Spatial.

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A family of foundation models, trained on real-world data to establish ground truth for physical AI.
ReconstructionMetric-scale splats and aligned meshes of real places, from a single room to city-scale.LocalizationPrecise vision-based positioning, even where GPS doesn’t work.UnderstandingObjects, change over time and answers at every 3D point of a real place.
Where people and machines work in the real world.
Oil and gasIntelligent field operations.Defense and intelligenceMission rehearsal and GPS-denied operations.Embodied AIReal-to-sim training for robots.
The team, partners and resources behind Niantic Spatial.
About
About usEventsCareers
Partners
Developer partner programAcademic and community partnersPartner with us
Guides and support
Scaniverse FAQNiantic Spatial SDK docsDeveloper forums
Scaniverse appCapture and reconstruct real places.PricingPlans for teams and enterprises.
Contact sales

Query the world, track changes, and predict what's next.

We’re building a semantic layer that turns 3D reconstructions into spatial knowledge so people and machines can understand what’s there, how objects relate, and what actions are possible.

Contact salesDiscover our research

One spatial data layer that connects what a place looks like with what it contains, how it works, and how it changes.

ReconstructionSplats and meshes of a place
Captures over timeFor change detection
QuestionsIn plain language
Your dataAsset lists and records
Understanding
VocabularyOpen-set, no retraining
MeaningAt every 3D point
InterfaceNatural language and API
Per-point semanticsGeometry, material, context
Located answersTied to a place
AssetsFound remotely
ChangeWhat moved, appeared or degraded

From 3D geometry to real-world understanding

  1. Spatially grounded semantics
    Each point carries a descriptor for geometry, material and context. Understand objects, materials, and relationships in their real-world context, with insights tied to precise 3D locations and traceable to the underlying geometry and imagery.
  2. Open-vocabulary understanding
    Move beyond predefined categories to query specific objects and concepts in natural language, and reason about their spatial relationships, not just recognize their presence.
  3. Change over time
    Compare captures to investigate what appeared, disappeared, or moved, grounding each change in its location and surrounding context so teams can understand more than a visual difference.

What spatial understanding makes possible

Get answers from your environment in plain language“Where does vegetation encroach on power lines?” “Which valves are above two meters?” Answers come back tied to a location.
Locate and inspect assets from anywhereLocate equipment, inspect it in 3D, and give remote experts and field crews the same view without unnecessary site visits.

Industries we support

Oil and GasRemote inspection, asset finding and change detection for plants, pipelines and utilities.Explore oil and gas Defense and IntelligenceA shared operational picture of terrain and sites for mission-critical decisions.Explore defense Embodied AIScene understanding so robots act on what things are, not just where they are.Explore embodied AI

Related

ReconstructionReconstruct the real world from multi-sensor data including off the shelf cameras as geometrically accurate, georeferenced 3D meshes and Gaussian splats for measurement, visualization, simulation, and spatial understanding.LocalizationPosition cameras, robots, and people within the reconstructed environment using visual localization even where GPS is unavailable or unreliable. A shared coordinate system connects navigation and field workflows to the same real-world site.

Frequently Asked Questions

What does semantic understanding add to a 3D reconstruction?

Reconstruction captures a place’s geometry and appearance. Semantic understanding connects that geometry to objects, materials, and spatial relationships so people, AI agents, and machines can search and reason about a real place, rather than simply view it.

What kinds of questions can I ask?

Our research demonstrations show searches for specific objects and measurements within reconstructed spaces. The broader goal is to answer questions such as “What is blocking this entrance?” or “Can a robot pass through here?” with answers grounded in the site’s 3D geometry and location.

Is it limited to predefined object categories?

Our research uses open-vocabulary models rather than a fixed list of labels. This enables searches for specific objects and concepts expressed in natural language, with spatial context helping explain where things are and how they relate, not just which category they belong to.

Can it show what changed between captures?

Our change-detection research compares aligned 3D reconstructions of the same space to surface and describe differences between captures. Working demonstrations show how this approach can reveal what moved, appeared, or degraded. It compares captured states, rather than providing continuous live monitoring.

Is semantic understanding available today?

Semantic understanding is currently in research, with working demonstrations of object-level queries, site search, and change detection. Reconstruction and visual positioning are available today; semantic capabilities are being developed on that foundation. Contact our team to discuss your use case and the current capabilities.