Consented first-person (POV) video, LiDAR, and scene annotation data that teaches vehicles and robots how the real world works.
Autonomous systems fail on what they haven't seen. We produce the diverse, real-scene data that closes those gaps.
First-person footage of real driving, walking, and task execution, consented and filmed by vetted contributors on their own devices.
3D bounding boxes, object segmentation, and ground-truth labeling for point-cloud perception.
Traffic elements, pedestrian intent, road conditions, and activity segmentation, labeled to your taxonomy.
Perception models for detection, prediction, and planning across real road scenarios.
Egocentric demonstrations of grasping, tool use, and household tasks for imitation learning.
First-person footage of sorting, packing, and delivery workflows for mobile robots.
Yes. 3D bounding boxes, object segmentation, and ground-truth labeling for point-cloud perception stacks.
Yes. Vetted contributors film consented egocentric (POV) footage of driving, walking, and task execution on their own devices.
Diverse, real-scene data: edge cases and real-world perception data that close the gaps where autonomous systems fail.
Autonomous systems fail on what they haven't seen; real-scene, consented data from XYNTRIQ's collection network reduces those gaps.
Into perception stacks for autonomous vehicles and robotics. The use cases are detailed on the page, from edge-case coverage to production datasets.
XYNTRIQ