20 Categories of LiDAR Point Cloud Processing Algorithm

1.Filtering algorithms: These algorithms are used to remove noise, outliers, or unwanted points from the point cloud data.
2.Segmentation algorithms: These algorithms group points together based on their attributes (e.g., color, intensity, reflectance, etc.) to identify objects or features in the scene.
3.Clustering algorithms: These algorithms group points together based on their spatial proximity to identify objects or features in the scene.
4.Classification algorithms: These algorithms assign semantic labels (e.g., ground, vegetation, building, etc.) to the points in the point cloud data.
5.Registration algorithms: These algorithms align multiple point clouds acquired from different viewpoints or at different times to create a unified representation of the scene.
6.Reconstruction algorithms: These algorithms create a 3D model of the scene from the point cloud data.
7.Surface modeling algorithms: These algorithms fit surfaces or geometries to the point cloud data to create a more compact representation.
8.Feature extraction algorithms: These algorithms identify salient features of objects in the point cloud data, such as edges, corners, or keypoints.
9.Normal estimation algorithms: These algorithms estimate surface normals at each point in the point cloud data to capture local surface geometry.
10.Contour detection algorithms: These algorithms identify contours or boundaries of objects in the point cloud data.
11.Segmentation refinement algorithms: These algorithms refine segmentation results by incorporating additional cues, such as texture or context.
12.Object detection algorithms: These algorithms identify objects of interest in the point cloud data, such as cars, pedestrians, or traffic signs.
13.Object tracking algorithms: These algorithms track objects of interest over time using multiple point clouds acquired at different time steps.
14.Scene understanding algorithms: These algorithms analyze the point cloud data to infer higher-level properties of the scene, such as the layout or functionality of the environment.
15.Anomaly detection algorithms: These algorithms detect abnormal or unexpected patterns in the point cloud data that may indicate anomalous behavior or events.
16.Change detection algorithms: These algorithms compare multiple point clouds acquired at different times to detect changes in the scene, such as new objects or changes in object position.
17.Compression algorithms: These algorithms compress the point cloud data to reduce storage or transmission bandwidth.
18.Visualization algorithms: These algorithms generate visualizations of the point cloud data to aid in human interpretation.
19.Quality assessment algorithms: These algorithms evaluate the quality of the point cloud data, such as accuracy, completeness, or consistency.
20.Calibration algorithms: These algorithms calibrate the lidar sensor to correct for systematic errors or drift.