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Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).

Point clouds are used for many purposes, including creating 3D CAD models for manufactured parts, metrology and quality inspection, and a multitude of visualization, animation, rendering, and mass customization applications.

The process of segregating LIDAR points according to the type object from which they reflected is termed “Classification” since we are labeling each point according to the class of reflecting object. 

Point Cloud Classification

LiDAR Point Cloud Data Acquisition

Airborne LiDAR system integrates GPS (Global Positioning System), IMU (Inertial Measurement Unit) and a laser scanner, which are mounted on aircrafts or Unmanned Aerial Vehicles (UAV). Three-dimensional coordinates and intensity information of ground objects can be obtained by using airborne LiDAR system and point cloud data thus are generated.

M&A data collection enables the capture of ancillary data such as position (X,Y,Z) and orientation (Pitch, Roll, Yaw) system (POS). We then combine in a set of (usually) post-processing steps to generate a pseudo-random point cloud in the spatial reference system (SRS).

We use point cloud processing software that recognize objects objects in previously selected segments and classify them by criteria such as shape and location and whether they’re mobile or static. In such a way, our client’s 3D point cloud software can classify objects like road signs, trees, roads, vehicles, buildings, bridges, and traffic lights. This 3D point cloud technology can cope with billions of data points, classifying them as real-world objects and assigning characteristics like color, geographic coordinates, angle, and speed. After objects are labeled, users can build and view a 3D model of an entire city or any landscape that needs to be digitally represented and render it right in a web browser.

We use a common file format called LAS (as in LASer) which contains, in addition to X, Y,Z attributes, slots for other information such as Classification