Open AccessBoundary‐enhanced supervoxel segmentation for sparse outdoor LiDAR data
Author(s) -
Song Soohwan,
Lee Honggu,
Jo Sungho
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.3249
Subject(s) - lidar , boundary (topology) , point cloud , segmentation , artificial intelligence , computer science , cluster analysis , computer vision , ranging , object (grammar) , key (lock) , pattern recognition (psychology) , image segmentation , point (geometry) , remote sensing , mathematics , geography , geometry , mathematical analysis , telecommunications , computer security
Voxelisation methods are extensively employed for efficiently processing large point clouds. However, it is possible to lose geometric information and extract inaccurate features through these voxelisation methods. A novel, flexibly shaped ‘supervoxel’ algorithm, called boundary‐enhanced supervoxel segmentation, for sparse and complex outdoor light detection and ranging (LiDAR) data is proposed. The algorithm consists of two key components: (i) detecting boundaries by analysing consecutive points and (ii) clustering the points by first excluding the boundary points. The generated supervoxels include spatial and geometric properties and maintain the shape of the object's boundary. The proposed algorithm is tested using sparse LiDAR data obtained from outdoor urban environments.