Image segmentation

Geology

Point cloud

Pattern recognition (psychology)

Terrain

Segmentation-based object categorization

Lidar

Scale-space segmentation

Artificial intelligence

Geography

Region growing

Computer science

Cartography

Computer vision

Remote sensing

Segmentation

This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e.g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e.g. Velodyne scans) is addressed using ground models of non-constant resolution either providing a continuous probabilistic surface or a terrain mesh built from the structure of a range image, both representations providing close to real-time performance. All the algorithms are tested on several hand labeled data sets using two novel metrics for segmentation evaluation.

On the segmentation of 3D LIDAR point clouds