ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 135–141, 2018
https://doi.org/10.5194/isprs-annals-IV-3-135-2018
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 135–141, 2018
https://doi.org/10.5194/isprs-annals-IV-3-135-2018

  23 Apr 2018

23 Apr 2018

A SUPER VOXEL-BASED RIEMANNIAN GRAPH FOR MULTI SCALE SEGMENTATION OF LIDAR POINT CLOUDS

Minglei Li Minglei Li
  • College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Keywords: LiDAR Point Clouds, Multi Scale Segmentation, Geometry Processing, Riemannian Graph, Super Voxels

Abstract. Automatically segmenting LiDAR points into respective independent partitions has become a topic of great importance in photogrammetry, remote sensing and computer vision. In this paper, we cast the problem of point cloud segmentation as a graph optimization problem by constructing a Riemannian graph. The scale space of the observed scene is explored by an octree-based over-segmentation with different depths. The over-segmentation produces many super voxels which restrict the structure of the scene and will be used as nodes of the graph. The Kruskal coordinates are used to compute edge weights that are proportional to the geodesic distance between nodes. Then we compute the edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths between super voxel nodes on the scene surface. The final segmentation results are generated by clustering similar super voxels and cutting off the weak edges in the graph. The performance of this method was evaluated on LiDAR point clouds for both indoor and outdoor scenes. Additionally, extensive comparisons to state of the art techniques show that our algorithm outperforms on many metrics.