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3D LiDAR annotation (Light Detection and Ranging) enables you to label, visualize and associate objects across 3D point clouds for all types of LiDARs. Autonomous vehicles, drones and agriculture all use this technique.
LiDAR is one of the most important services required for Autonomous Vehicles. Fully automated vehicles utilize LiDAR extensively as they require large amounts of datasets to label. Labeling is an important aspect of deep learning, because it involves the labeling of a large volume of data. We offer you 3D LiDAR annotation support in three different formats, in addition to the expertise that we have in line annotation, image annotation and video annotation.
LiDAR sensors understand 3D point clouds by marking each point on an object, enabling the identification of precise objects in both outdoor and indoor environments. A 1cm object can be annotated with 3D box annotations by defining each point on the object.
With the help of point cloud semantic segmentation, any object can be analyzed for its additional attributes, which can assist the learning processes. A 3D point cloud map can be used to distinguish different types of lanes efficiently, which is highly useful for self-driving cars. The differentiation will enable them to provide the roads with highly precise 3D modelling, allowing them to provide safer and more accurate driving directions.
At Infosearch, we use the tool of 3D point cloud labeling, such as the roll, yaw, pitch, and heading angles, to detect the objects and tracks efficiently and precisely. Using this tool, you can label a variety of objects much more effectively, thus upgrading the level of your models.
A road lane can be detected and the objects tracked within the frame, enabling the labeling of moving objects across multiple frames. Interactive annotation using semi-automated processes can be used to label any 3D object in just one click. To make the process of labeling much quicker, our team will utilize advanced annotation tools including linear interpolation and various other advanced tools.
Using machine learning, annotation support manages the most complex workflows associated with selecting data, annotation, and modeling. We deliver quality point cloud annotation, 2D mapping, 3D object tagging, and semantic segmentation of point clouds with our experienced annotators. In addition, annotation support helps improve the quality and security of the model provided by the whole machine learning process.