Refinement of LiDAR point clouds using a super voxel based approach

Abstract We propose a new approach for automatic refinement of unorganized point clouds captured by LiDAR scanning systems. Given a point cloud, our method first abstracts the input data into super voxels via over segmentations, and then builds a K-nearest neighbor graph on these voxel nodes. Abstracting into voxel representation provides a means to generate an elastic wireframe over the original data. An iterative resampling method is then introduced to project resampling points to all potential surfaces considering repulsion constraints from both interior and exterior of voxels. Our point consolidation process contributes to accurate normal estimation, uniform point distribution, and sufficient sampling density. Experiments and comparisons have demonstrated that the proposed method is effective on point clouds from a variety of datasets.