Turn Drone Imagery into 3D Intelligence
Niantic Spatial transforms drone imagery into high-fidelity, geometrically accurate 3D reconstructions of the real world.
From raw aerial capture to actionable spatial data in one seamless workflow
Our pipeline stitches together multiple drone scans of real-world spaces into geometrically accurate 3D Gaussian splats, delivering extreme detail and centimeter-level measurement from any standard camera.
Niantic Spatial and Spexi Partner to Turn Drone Imagery Into Intelligence for Physical AI
This partnership pairs Spexi's next-generation aerial data network with Niantic Spatial's industry-leading reconstruction technology, on demand.
Drone Imagery to 3D Reconstruction of a Construction Site
Your Drone-to-3D Pipeline On Demand
Commission drone captures and receive high-fidelity 3D reconstructions through Niantic Spatial's Reconstruction API in one seamless workflow, built for the decisions your customers need to make.
Infrastructure inspection. Identify defects, monitor wear, and prioritize maintenance across bridges, towers, pipelines, and roadways at centimeter-level accuracy.
Energy site analysis. Survey solar farms, wind installations, and transmission corridors at scale — turning aerial data into operational insight.
Asset management. Maintain a living 3D record of facilities and sites — accurate enough to plan against, detailed enough to act on.
Training data for physical AI. Power robotics, autonomy, and AI agents with metric-scale reconstructions for simulation, location, and training.
Compilation of 3D Gaussian Splats from Drone Imagery
Why Niantic Spatial, Not Open Source
Open-source splat tools optimize for photorealistic views. Niantic Spatial's reconstruction stack is engineered for measurement, scale, and production, turning drone captures into geometrically consistent 3D your customers can build on.
Geometrically consistent splats. Depth- and geometry-constrained Gaussian splats free of the floor and surface penetration, ghosting at occlusion boundaries, and holes in low-texture regions that compromise open-source output.
LiDAR-quality output, no LiDAR. Proprietary depth models reconstruct featureless surfaces (e.g. painted metal, concrete, water) where conventional photogrammetry degrades.
Scale-accurate by default. Meshes extracted directly from splats at metric scale ready for measurement, CAD, and simulation without a separate scaling step.
Engineered for city-scale. Tiled cloud processing with interactive real-time rendering, well beyond the room-scale ceiling of open-source implementations.