Cell-based Automatic Deformation Computation by Analyzing Terrestrial LIDAR Point Clouds

This paper presents a cell-based approach for computing the deformation of a monitored object by analyzing point cloud data from terrestrial lidar. This approach can automatically generate an informative deformation description (called "deformation map") with distinctive deformation characteristics for different partial areas. The approach consists of three major computing steps: (a) "split" - the space of the monitored object is divided into 3D uniform cells, (b) "detect" - deformation parameters for each cell (called "meta-deformation") are estimated by comparing the point clouds in the cell sampled at Epochs I and II, and (c) "merge" - the adjacent cells with similar meta-deformation are combined together in a partial area with a consistent "sub-deformation." The main contributions of this paper ore: (a) a hybrid deformation model for incremental and comprehensive deformation representation, including meta-deformation, sub-deformation and deformation map, (b) a systematic procedure of "split-detect-merge" to automatically and gradually estimate the hybrid deformation model, and (c) a complete validation with a couple of synthetic and real datasets.