scholarly journals Generating Absolute-Scale Point Cloud Data of Built Infrastructure Scenes Using a Monocular Camera Setting

2015 ◽  
Vol 29 (6) ◽  
pp. 04014089 ◽  
Author(s):  
Abbas Rashidi ◽  
Ioannis Brilakis ◽  
Patricio Vela
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Baoyun Guo ◽  
Jiawen Wang ◽  
Xiaobin Jiang ◽  
Cailin Li ◽  
Benya Su ◽  
...  

Due to the memory limitation and lack of computing power of consumer level computers, there is a need for suitable methods to achieve 3D surface reconstruction of large-scale point cloud data. A method based on the idea of divide and conquer approaches is proposed. Firstly, the kd-tree index was created for the point cloud data. Then, the Delaunay triangulation algorithm of multicore parallel computing was used to construct the point cloud data in the leaf nodes. Finally, the complete 3D mesh model was realized by constrained Delaunay tetrahedralization based on piecewise linear system and graph cut. The proposed method performed surface reconstruction on the point cloud in the multicore parallel computing architecture, in which memory release and reallocation were implemented to reduce the memory occupation and improve the running efficiency while ensuring the quality of the triangular mesh. The proposed algorithm was compared with two classical surface reconstruction algorithms using multigroup point cloud data, and the applicability experiment of the algorithm was carried out; the results verify the effectiveness and practicability of the proposed approach.


Author(s):  
K. Liu ◽  
J. Boehm

Point cloud data plays an significant role in various geospatial applications as it conveys plentiful information which can be used for different types of analysis. Semantic analysis, which is an important one of them, aims to label points as different categories. In machine learning, the problem is called classification. In addition, processing point data is becoming more and more challenging due to the growing data volume. In this paper, we address point data classification in a big data context. The popular cluster computing framework Apache Spark is used through the experiments and the promising results suggests a great potential of Apache Spark for large-scale point data processing.


2019 ◽  
Vol 8 (8) ◽  
pp. 343 ◽  
Author(s):  
Li ◽  
Hasegawa ◽  
Nii ◽  
Tanaka

Digital archiving of three-dimensional cultural heritage assets has increased the demand for visualization of large-scale point clouds of cultural heritage assets acquired by laser scanning. We proposed a fused transparent visualization method that visualizes a point cloud of a cultural heritage asset in an environment using a photographic image as the background. We also proposed lightness adjustment and color enhancement methods to deal with the reduced visibility caused by the fused visualization. We applied the proposed method to a laser-scanned point cloud of a high-valued cultural festival float with complex inner and outer structures. Experimental results demonstrate that the proposed method enables high-quality transparent visualization of the cultural asset in its surrounding environment.


Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.


Sign in / Sign up

Export Citation Format

Share Document