SnA offers Lidar annotation solutions that enhance AI, machine learning, and data processing efforts.
Lidar (Light Detection & Ranging) cameras are an essential sensor for geospatial technology, autonomous technology, and many other industry applications. Lidar utilizes lasers, scanners, and specialized GPS receivers to calculate distances to a given object. Annotating 3D point cloud data from Lidar cameras is a challenging and time-consuming task that requires specialized tools and an expert-level understanding of data annotation. 3D point cloud annotation can be combined with image annotation to train computer vision and other deep learning models to perform a variety of tasks.
Lidar box labeling assists autonomous vehicles in recognizing objects from 3D images. By using 3D Lidar sensor data, machines are trained to precisely identify annotated objects rather than generic ones within a specific environment.
Lidar, or 3D point cloud semantic segmentation, is a fundamental component of autonomous technology, involving the assignment of a class label to every data point in the input.
Detailed features can be captured using 3D point cloud annotation, enabling models to detect objects and differentiate between multiple 3D items within a scene.
Lidar or 3D point cloud landmark annotation involves labeling key anatomical or structural points to create precise datasets that define the shapes of various objects, enabling machine learning algorithms to detect smaller features.
Lidar or 3D point cloud polygon annotations are ideal for training object localization and detection models on items such as logos, signboards, and various human postures, using precisely drawn polygons that tightly fit the objects.
Lidar or 3D point cloud polyline annotation assists autonomous vehicles in detecting road lanes on city streets and highways. SnA’s annotators label raw data and images of highways and urban roads to make the road layouts clear and recognizable for self-driving cars.
