Field atlas of interior geometries.
Crowdsourced LiDAR scans from any phone, reconstructed into robotics-ready datasets.
graticule 30°
35.7°N 139.7°E
Four specimens from a single walk through one room.
Depth maps.
Per-pixel distance to the world, registered against device pose. Fused from on-device LiDAR and photogrammetric stereo.
Spatial layouts.
Watertight room geometry, walls, doors, openings, with semantic room labels and per-room area in SI units.
Indoor scenes.
Furniture, surfaces, and objects, dense-reconstructed and labelled in 3D, anchored to a single right-handed room frame.
Movement trajectories.
6-DOF pose at 60 Hz. Every step, turn, and gesture, calibrated against the scene with IMU sensor fusion and loop closure.
From a phone in a pocket to a tensor in a training run.
Walk the space.
Press record and walk the room. On-device ARKit / ARCore runs lightweight SLAM. Only keyframes, sparse cloud, and pose graph are kept. Faces and plates are blurred before anything leaves the phone.
Reconstruct in the cloud.
Chunks arrive and dense-reconstruct with COLMAP + Open3D, anchored to scale by LiDAR depth. Multi-session fusion merges every scan of the same place via ICP into one scene. On T4 GPUs, 2 to 5 minutes each.
Export to your stack.
Pull the unified scene as a ROS bag, PLY/PCD, or NeRF-ready COLMAP folder, each with a quality-scored JSON sidecar. Served over REST and as live MCP resources for agents.
One scan, every format your models speak.
Real rooms, exported natively into the robotics and reconstruction stacks.
Every scene is reconstructed once and exported into the formats the ecosystem already runs on. No custom adapters, no lossy conversions. Pull it over a plain REST call, or subscribe to live phones as spatial:// resources over MCP.
- .bagROS bagsensor_msgs/PointCloud2 + nav_msgs/Path, ready to replay.robot nav / sim
- .ply/.pcdOpen3DDense, voxel-normalised clouds with estimated normals.processing / viz
- /sparseCOLMAPSparse + dense folders, LiDAR-anchored scale.nerfstudio / ngp
- .jsonMetadataDevice, depth_source, 0 to 100 quality score per scene.indexing / QA
1# 1 / create a session, stream keyframe chunks 2POST /sessions # to id 3POST /sessions/{id}/chunks # gzip, 5 MB 4POST /sessions/{id}/complete # trigger fuse 5 6# 2 / poll until the scene is reconstructed 7GET /sessions/{id}/status 8# score 87 / accepted / 218,440 pts / lidar 9 10# 3 / export in the format you need 11GET /sessions/{id}/export/ros 12GET /sessions/{id}/export/ply 13GET /sessions/{id}/export/colmap # NeRF-ready 14 15# or, live, over MCP: 16subscribe spatial://hub.local/scn_4a7c
Every scene is scored before it ships.
- Point-cloud geometry & depth maps
- Pose trajectory
- Device model & depth_source
- Human faces, blurred on-device
- License plates, blurred on-device
- Precise GPS (city-level only) & personal info
Scan more. Unlock more. Keep the credit.
Bronze
Silver
Gold
Verified
For everything that has to move through space.
Pretrain manipulation policies on the rooms they will actually work in.
Crowd-captured scans as a domain-randomization source for imitation learning, behavior cloning, and sim-to-real bridges. Real rooms, not synthetic ones.
home / office / café / warehouse
Ground a model in the room it is standing in.
Stream the active scan into an agent's context via MCP. “Where is the coffee mug?” becomes a question the model can actually answer.
resources
Indoor maps without GPS.
Trajectory and layout data train routing policies for delivery bots, vacuums, and AR wayfinding through real homes and offices.
A foundation model for space itself.
Geometry-native pretraining at scale, the way language models treated the open web, but for the volumetric structure of every human environment.
“Every smartphone, a robot training device.”
Open spatial data
belongs to everyone.
SpatialOS is Apache-2.0, MCP-native, and built in public. Bring a phone. Bring a robot. Help map the world for the machines that will live in it.
About
An open platform turning the phones in our pockets into the world's largest spatial-data corpus for robots and embodied AI.