Point Cloud Processing
Point cloud processing is a field of computer vision and computational geometry that involves analyzing and manipulating sets of data points in 3D space, typically captured by sensors like LiDAR, depth cameras, or photogrammetry. It focuses on tasks such as filtering, segmentation, registration, feature extraction, and surface reconstruction to interpret and utilize 3D spatial data. This technology is essential for applications like autonomous vehicles, robotics, augmented reality, and 3D modeling.
Developers should learn point cloud processing when working with 3D spatial data in fields such as autonomous driving (for obstacle detection and mapping), robotics (for environment perception), and AR/VR (for scene understanding). It is crucial for handling raw sensor data from devices like LiDAR scanners, enabling tasks like object recognition, terrain analysis, and creating detailed 3D models from real-world scans.