Light Detection and Ranging(LiDAR) Annotation
3D Point Cloud Labeling Services for LIDARs
LiDARs – the most essential sensor for autonomous vehicles, operating at higher levels (L4-L5) of autonomy. Using deep learning algorithms requires a huge amount of training data labeled through point cloud annotation. Annotating the LiDAR(Light Detection and Ranging) point cloud data is a challenging task due to its low resolution, complex annotation process, and the fact that it is time-consuming. However, AI Wakforce is your One-Stop Solution, as we perform this job perfectly with expertise in image annotation services to create training data for machine learning algorithms.
3D Point Cloud Annotation for LiDARs
In cloud annotation, the object up to 1 cm can be annotated with 3D boxes labeling the objects at every single point. To make the objects recognizable in both environments indoor and outdoor, 3D point cloud annotation is best suitable for precise detection through LiDAR sensors. Using the right tool and technique, our in-house trained workforce is capable of annotating any type of LiDAR generated data.
Point Cloud Semantic Segmentation
LiDAR point cloud segmentation is the technique used to classify an object having additional attributes that any perception model can detect for learning. For self-driving cars, 3D point cloud annotation services help them to distinguish different types of lanes in a 3D point cloud map in order to annotate the roads for safe driving with more precise visibility using 3D orientation.
Point Cloud to Detect Objects with 3D Boxes
3D boxes to detect the objects with more precision and track including the single points with excellence to gather details like size, location, speed, yaw, pitch with class, etc. AI Wakforce data annotation team uses the most advanced 3D point cloud labeling tool to label different types of objects including dimensions of other objects of interest like bicycles & pedestrians in drivable lanes.
LiDARs Point Cloud Annotation Detecting Lane
