⬇️Export LiDAR Datasets
Once annotation and validation are complete, export the finalized LiDAR dataset from Datasets > [Project Name].
Before exporting, review the right-side configuration panel.
Pre-Export Validation Checklist
1. Release Version
Confirm the correct version (e.g., v1.0) is selected.
Click Release New Version if you need to freeze the current state.
Each release version captures:
All annotated frames
Object instances and class mappings
Frame-level metadata
Media configuration
Only released versions can be exported.
2. Annotation Summary
Verify object counts (e.g., Pedestrian, Car, Cyclist).
This ensures:
No missing annotations
No accidental deletions
Expected class distribution before export
3. Annotation Opacity (Validation Tool)
The Annotation Opacity slider adjusts bounding box transparency (visual only — does not affect export).
Use it to:
Check 3D box alignment with the LiDAR point cloud
Verify box tightness and orientation
Inspect overlapping objects
Best practice:
Lower opacity → inspect point cloud density
Higher opacity → validate box placement
4. Media Attributes
The Media Attributes section displays essential metadata associated with the selected LiDAR file. This information ensures data consistency, traceability, and export readiness.
Best practice: Review and standardize all required attributes before export to ensure data quality and consistency.
Export Process
1. Open Export Modal
Click Download (top-right).
2. Select Version
Example: v1.0 Always confirm the intended release version.
3. Select Format (LiDAR Supported)
Available formats:
KITTI
nuScenes
Supervisely
Custom JSON
Custom CSV
ROS Bag
Export Format Guide
(I) KITTI
Industry-standard format for:
3D Object Detection
Autonomous Driving
Perception Model Training
Includes annotations and calibration files compatible with KITTI-based pipelines.
(II) nuScenes
Designed for multi-sensor autonomous driving datasets.
Supports:
LiDAR
Camera
Sensor calibration
Timestamp synchronization
Rich metadata
(III) Supervisely
Exports annotations in a structure compatible with the Supervisely platform for continued annotation or dataset management.
(IV) Custom JSON
Flexible structured format for custom machine learning pipelines.
Ideal for:
Internal tools
Data processing
AI pipelines
Custom integrations
(V) Custom CSV
Exports annotation data in tabular format.
Useful for:
Quality Assurance
Analytics
Dataset auditing
Reporting
(VI) ROS Bag
Exports the annotated dataset in ROS Bag-compatible format, enabling seamless integration with robotics development workflows.
Ideal for:
Playback of recorded LiDAR sensor data
Testing and debugging perception algorithms
Robot simulation and validation
Offline development without requiring physical hardware
ROS and ROS 2 ecosystem integration
ROS Bag export allows developers to replay synchronized sensor data together with annotations, making it easier to validate object detection, tracking, localization, and autonomous navigation pipelines.
Export Includes
Depending on format, the package may contain:
3D bounding box parameters (x, y, z, length, width, height, rotation/yaw)
Class labels
Object instance IDs
Frame metadata
Sensor metadata
Multiview alignment data (if applicable)
Media attributes (format-dependent)
ROS Bag files (.bag or ROS 2 bag structure) containing synchronized LiDAR sensor recordings (for ROS Bag export)
Final Step
After confirming version and format:
👉 Click Download
The system generates a structured LiDAR dataset package ready for training or deployment.
Exports include:
3D bounding box coordinates
Class labels
Frame metadata
Multiview alignment data (if applicable)
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