点击次数：

所属单位：电子信息工程学院

发表刊物：ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

关键字：Point cloud Octree Super voxel Data refinement

摘要：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. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

备注：卷: 143 页: 213-221

ISSN号：0924-2716

是否译文：否

发表时间：2018-09-01