LiDAR Data Labelling
LabelOps provides LiDAR data labeling to identify objects and define their spatial boundaries within 3D point clouds. Using appropriate 3D annotation techniques, we prepare structured training datasets that help AI models recognise vehicles, pedestrians, cyclists, and other relevant objects in driving environments.
- 3D Bounding Box Annotation
- 3D Point Cloud Labeling
- Point Cloud Segmentation
- Sensor Fusion Data Annotation
Frequently Asked Questions
LiDAR point cloud annotation is the process of labeling 3D point cloud data generated by LiDAR sensors. These labels identify objects and their spatial boundaries, helping prepare datasets for training and evaluating computer vision models.
Annotated LiDAR datasets can help train and evaluate autonomous driving models to detect and understand objects such as vehicles, pedestrians, and cyclists in 3D environments, depending on the annotation categories and project requirements.
3D bounding box annotation involves placing three-dimensional boxes around objects in point cloud data to represent their position, dimensions, and orientation. These labels are commonly used to prepare training datasets for 3D object detection models.
2D bounding boxes mark an object's location within a two-dimensional image, while 3D bounding boxes represent its spatial position and dimensions in three-dimensional data. The appropriate technique depends on the sensor data and the target AI application.
Depending on the project guidelines and supported annotation categories, objects may include vehicles, pedestrians, cyclists, and other relevant road users or environmental elements.
Sensor fusion data annotation involves labeling and, where supported, aligning data from multiple sensors, such as LiDAR and cameras. It helps prepare datasets for AI systems that combine complementary sensor information. Multi-sensor synchronization and annotation should be confirmed as part of the project scope.
