Autonomous Vehicles
End-to-end perception datasets covering object detection, multi-frame tracking, and free-space mapping.
Advanced & 3D Labeling
3D Point Cloud Annotation Services
Cuboid labeling, segmentation, and sensor-fusion annotation for LiDAR-based perception systems.
Overview
3D perception data leaves little room for error — sparse point returns, sensor drift, and object boundaries that must hold up to centimeter-level scrutiny. Vidyut Data's 3D annotation teams work directly with LiDAR point clouds and fused camera-LiDAR scenes, handling cuboid placement, point-level segmentation, and cross-sweep object tracking. Every output passes through calibrated annotation tooling and a physical-plausibility QA pass — checking that objects sit where they should, don't float above the ground plane, and don't bleed into neighboring pedestrians or structures.
What's included
- ✓3D cuboid annotation with orientation, dimensions, and object attributes
- ✓Point-level segmentation, both semantic (class) and instance (individual object)
- ✓Multi-sweep object tracking to maintain identity across frames
- ✓Camera-to-LiDAR correspondence for sensor fusion pipelines
- ✓HD map feature labeling — lane boundaries, signage, curb lines
- ✓Keyframe-based interpolation with manual review for occluded objects
Use cases
Robotics & Autonomous Mobile Robots
Indoor 3D environment labeling to support navigation, obstacle avoidance, and manipulation tasks.
HD Mapping
Precision lane-level map feature extraction from mobile mapping vehicle data.
Infrastructure Inspection
Object and asset identification from aerial and ground-based 3D scan data.
Frequently asked questions
How do you keep object identity consistent across a full LiDAR sequence?
We track each object across sweeps with persistent IDs and run per-track review, so a car labeled in frame 1 stays the same car in frame 500 — identity switches and drift are caught before delivery.
Can you handle fused camera-LiDAR data, not just raw point clouds?
Yes. We link 2D image annotations to 3D points for sensor-fusion pipelines, and our tooling works with calibrated multi-sensor setups.
What sensor formats and coordinate systems do you support?
We work with common LiDAR formats and your calibration/coordinate conventions, and deliver in the schema your perception stack expects. We confirm this during the pilot.
Can we start with a small pilot before committing?
Yes, and we recommend it — a paid pilot on your real data lets you judge cuboid tightness and QA quality before scaling.
Ready to scale your 3d point cloud annotation services?
Start with a pilot batch — see the quality of the data before you commit.