Color Balance Method of Dense Point Cloud in Landslides Area Based on UAV images

2015 ◽

Vol XL-1-W5 ◽

pp. 43-46 ◽

Cited By ~ 7

Author(s):

F. Alidoost ◽

H. Arefi

Keyword(s):

Urban Areas ◽

Point Cloud ◽

3D Model ◽

Visual Assessment ◽

Mesh Model ◽

Building Facades ◽

Dense Point ◽

Global Matching ◽

Complex Building ◽

Uav Images

Nowadays, with the development of the urban areas, the automatic reconstruction of the buildings, as an important objects of the city complex structures, became a challenging topic in computer vision and photogrammetric researches. In this paper, the capability of multi-view Unmanned Aerial Vehicles (UAVs) images is examined to provide a 3D model of complex building façades using an efficient image-based modelling workflow. The main steps of this work include: pose estimation, point cloud generation, and 3D modelling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM) is applied on UAV images and a dense point cloud is generated. Then, a mesh model of points is calculated using Delaunay 2.5D triangulation and refined to obtain an accurate model of building. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough details of building based on visual assessment.

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QUALITY FEATURES FOR THE INTEGRATION OF TERRESTRIAL AND UAV IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w9-339-2019 ◽

2019 ◽

Vol XLII-2/W9 ◽

pp. 339-346 ◽

Cited By ~ 4

Author(s):

E. M. Farella ◽

A. Torresani ◽

F. Remondino

Keyword(s):

Point Cloud ◽

Ground Truth ◽

Internal Quality ◽

3D Reconstructions ◽

Ground Truth Data ◽

Dense Point ◽

Modena Cathedral ◽

Uav Images ◽

Filtering Procedure ◽

Quality Features

<p><strong>Abstract.</strong> The paper presents an innovative approach for improving the orientation results when terrestrial and UAV images are jointly processed. With the existing approaches, the processing of images coming from different platforms and sensors leads often to noisy and inaccurate 3D reconstructions, due to the different nature and properties of the acquired images. In this work, a photogrammetric pipeline is proposed to filter and remove bad computed tie points, according to some quality feature indicators. A completely automatic procedure has been developed to filter the sparse point cloud, in order to improve the orientation results before computing the dense point cloud. We report some tests and results on a dataset of about 140 images (Modena cathedral, Italy). The effectiveness of the filtering procedure was verified using some internal quality indicators, external checks (ground truth data) and qualitative visual analyses.</p>

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Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud

Sensors ◽

10.3390/s20185220 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5220

Author(s):

Shima Sahebdivani ◽

Hossein Arefi ◽

Mehdi Maboudi

Keyword(s):

3D Modeling ◽

Point Cloud ◽

3D Model ◽

Three Dimensional ◽

Point Clouds ◽

Rail Track ◽

Three Dimensional Modeling ◽

Railway Industry ◽

Dense Point ◽

Uav Images

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study.

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Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures

Remote Sensing ◽

10.3390/rs12182873 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2873 ◽

Cited By ~ 1

Author(s):

Elisa Mariarosaria Farella ◽

Alessandro Torresani ◽

Fabio Remondino

Keyword(s):

3D Reconstruction ◽

Point Cloud ◽

Typical Result ◽

Dense Point ◽

Quality Checks ◽

Aerial Vehicle ◽

3D Processing ◽

Uav Images ◽

Image Orientation ◽

Dense 3D Reconstruction

The paper presents an efficient photogrammetric workflow to improve the 3D reconstruction of scenes surveyed by integrating terrestrial and Unmanned Aerial Vehicle (UAV) images. In the last years, the integration of this kind of images has shown clear advantages for the complete and detailed 3D representation of large and complex scenarios. Nevertheless, their photogrammetric integration often raises several issues in the image orientation and dense 3D reconstruction processes. Noisy and erroneous 3D reconstructions are the typical result of inaccurate orientation results. In this work, we propose an automatic filtering procedure which works at the sparse point cloud level and takes advantage of photogrammetric quality features. The filtering step removes low-quality 3D tie points before refining the image orientation in a new adjustment and generating the final dense point cloud. Our method generalizes to many datasets, as it employs statistical analyses of quality feature distributions to identify suitable filtering thresholds. Reported results show the effectiveness and reliability of the method verified using both internal and external quality checks, as well as visual qualitative comparisons. We made the filtering tool publicly available on GitHub.

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