Zhihua Xu

Tridimensional Reconstruction Applied to Cultural Heritage with the Use of Camera-Equipped UAV and Terrestrial Laser Scanner

No single sensor can acquire complete information by applying one or several multi-surveys to cultural object reconstruction. For instance, a terrestrial laser scanner (TLS) usually obtains information on building facades, whereas aerial photogrammetry is capable of providing the perspective for building roofs. In this study, a camera-equipped unmanned aerial vehicle system (UAV) and a TLS were used in an integrated design to capture 3D point clouds and thus facilitate the acquisition of whole information on an object of interest for cultural heritage. A camera network is proposed to modify the image-based 3D reconstruction or structure from motion (SfM) method by taking full advantage of the flight control data acquired by the UAV platform. The camera network improves SfM performances in terms of image matching efficiency and the reduction of mismatches. Thus, this camera network modified SfM is employed to process the overlapping UAV image sets and to recover the scene geometry. The SfM output covers most information on building roofs, but has sparse resolution. The dense multi-view 3D reconstruction algorithm is then applied to improve in-depth detail. The two groups of point clouds from image reconstruction and TLS scanning are registered from coarse to fine with the use of an iterative method. This methodology has been tested on one historical monument in Fujian Province, China. Results show a final point cloud with complete coverage and in-depth details. Moreover, findings demonstrate that these two platforms, which integrate the scanning principle and image reconstruction methods, can supplement each other in terms of coverage, sensing resolution, and model accuracy to create high-quality 3D recordings and presentations.

A progressive morphological filter for point cloud extracted from UAV images

This study utilizes the unmanned aerial vehicle (UAV) to acquire high resolution images for feature matching, resulting in a point cloud. A progressive morphological filter is used to filter out nonground object points from point cloud. Multi-scale and different shape filter windows are adopted for the morphological filter to achieve good performance. The results show that multi-scale and multi-shape window can improve the performance of morphological filter compared with single direction filter window, as nonground objects cannot be completely removed with single direction filter window. With multi-shape or 2-D filter window, buildings can be effectively removed and ground points can be reserved.

Matching UAV images with image topology skeleton

We address the problem of efficient image matching for large, highly redundant photo collections with highly complex topologies, such as photos acquired by unmanned aerial vehicles (UAV), focusing on disaster monitoring. Our approach conducts a skeleton graph which simplifies the image topology with the consideration of image importance and topological relationship. We define the image with the highest importance weight as candidate, adding the remaining images referring to the topology skeleton. To conduct the skeletal graph, the image topology is first computed depending on the overlapping relationships between images. Experimental results show that our technique drastically limits the searching range that is for feature similarity computation, resulting in dramatic speed up. A final bundler adjustment is implemented in the procedure of scene reconstruction, and the completeness and accuracy are far more comparable to the traditional method.

Extraction of damaged building's geometric features from multi-source point clouds

There is no single sensor can acquire the complete information for disaster monitoring. This study investigates the applicability of registering multiple point clouds obtained from unmanned aerial vehicle (UAV) images and terrestrial laser scanning (TLS). Low attitude images with high overlaps were collected by an eight-rotor UAV platform and image-based 3D modeling techniques are used to generate 3D point cloud, covering most of roof information of the damaged buildings. TLS was used to collect the side information of the damaged buildings with multiple scans. Point clouds from the two platforms are iteratively registered using a method, from coarse to fine, to get complete geometry of the study area. Geometric features are subsequently extracted to help for the identification of damage degree of buildings. Experimental result shows that by analyzing the intersection lines of plane features, we can further detect the buildings inclination.

Triangular-prism-based algorithm on urban flood inundation simulation by employing dichotomy numerical solution

Triangular Irregular Network (TIN), especially Constrained Delaunay Triangular Irregular Network (CD-TIN), has been proven to be more accurate for the modeling of complex urban terrain, compared with raster represented digital elevation model (DEM). DEM with high precision is of importance to simulate urban flood inundation. In the paper, the triangular-prism sets are introduced to calculate the inundation water surface within each basin. It is worth mentioning that the explored dichotomy numerical solution is efficient on the simulation of the inundation depth. The case of ramp-shape urban terrain validates the correctness of the algorithm. And the application of the “7.21” storm in main campus of Beijing Normal University (BNU) verifies the usability and practicality of the algorithm. The cases demonstrate the feasibility of the proposed algorithm for simulation of urban flood inundation associated with urban surface runoff and drainage system capacity information.

Analysis of gully erosion hazard using high resolution terrestrial LiDAR

Gully erosion removes large volumes of soil and water from complex areas. Its difficult to quantify the loss of soil because the footprint of individual gullies is too complex to be captured by most common measures, such as general available digital elevation models (DEMs). LiDAR (Light Detection and Ranging) is a recent innovation technique widely used in topographic surveying, smart city, historic preservation, revere engineering, etc. Ground-based LiDAR has the potential to provide the required data with non-contacting, convenient, fast and high accuracy at lower cost. In this study, two different Terrestrial Laser scanners (TLS) were used to monitor an individual gully. Terrain complexity and volumetric estimates of the eroded sediment were produced by comparing the LiDAR-derived DEMs under different point cloud resolution. The result showed that the surface roughness and erosion sediment were increased with higher resolution point cloud. However, time-consuming and computational complexity was suddenly raised as dense point cloud provided more details information. This paper provides a new method for how to choose a moderate resolution in dealing with gully erosion in regional area.