Voxelization methods are extensively employed for efficiently processing large point clouds. However, it is possible to lose geometric information and extract inaccurate features through these voxelization methods. In this letter, we propose a novel, flexibly-shaped supervoxel algorithm, called boundary enhanced supervoxel segmentation, for sparse and complex outdoor light detection and ranging (LiDAR) data. 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.
Related publications
1. S Song, H Lee, S Jo, Boundary enhanced supervoxel segmentation for sparse outdoor LiDAR data, Electronics Letters, 50(25), 2014. [LINK][PDF]
