3D Point Annotation for All LiDARs
Label the objects at every single point with highest accuracy 3D point cloud annotation is capable to detect objects up to 1 cm with 3D boxes with definite class annotation. Used for autonomous vehicles to identify objects in the both environment indoor and outdoor. This 3D segmentation can also detect the object’s motion in a video.
Classify Objects and Detect Lanes
It can classify each object having the additional attribute that a perception model can detect for learning. Our 3D point cloud annotation services help to distinguish different types of lanes in 3D point cloud map to annotate the roads for safe driving of autonomous vehicles. Suitable for every object in 3D orientation with a class at high-level of accuracy.
Detect Objects with 3D Boxes
A 3D boxes for better object detection and tracking including the single points with quality to get the details like size, location, yaw, speed, pitch with class. We use most advance 3D point cloud labeling tool to support all types of objects including other objects of interest dimension including designated pedestrians and cycling drivable designated zones on the street.
3D object detection, which is made possible with data annotation & labeling, is widely used in robotics to avoid collisions with dynamic objects, such as humans, animals, and movable characters.
Using computer vision algorithms, annotating bounding boxes of everything around a vehicle allows the car to detect objects such as pedestrians, vehicles, traffic signs, and barriers.
Embedding annotations & appropriate labels in AI is a significant part of discovering links between genetic codes, powering surgical robots, and optimizing healthcare processes & productivity.
AI in Retail
Appropriately performed image annotation & data labeling can play a crucial role in AI implementation to automate while also helping retailers to enhance the customers’ shopping experience.
AI implementations enabling automated or assisted flight can be made easier and more accessible through image annotation performed at the backend with autonomous flying training data.
IoT sensors and bounding box annotations can provide real-time data for AI algorithms to contribute to agricultural efficiency and yield improvement with real-time insights from their fields.