scholarly journals A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner

2021 ◽  
Vol 31 (2) ◽  
pp. 303-312 ◽  
Author(s):  
Sarvesh Kumar Singh ◽  
Simit Raval ◽  
Bikram Banerjee
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Robust Approach ◽  
Cloud Data ◽  
Roof Bolts

scholarly journals Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

2019 ◽  
Vol 11 (6) ◽  
pp. 729 ◽  
Author(s):  
Shiyan Pang ◽  
Xiangyun Hu ◽  
Mi Zhang ◽  
Zhongliang Cai ◽  
Fengzhu Liu
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Laser Scanner ◽  
Graph Cuts ◽  
Aerial Images ◽  
Processing Unit ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Surface Models

Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.

scholarly journals DRAWING AND LANDSCAPE SIMULATION FOR JAPANESE GARDEN BY USING TERRESTRIAL LASER SCANNER

2015 ◽  
Vol XL-4/W5 ◽  
pp. 233-238
Author(s):  
R. Kumazaki ◽  
Y. Kunii
Keyword(s):  
Point Cloud ◽  
Landscape Architecture ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Contour Lines ◽  
Japanese Garden ◽  
Laser Scanners

Recently, many laser scanners are applied for various measurement fields. This paper investigates that it was useful to use the terrestrial laser scanner in the field of landscape architecture and examined a usage in Japanese garden. As for the use of 3D point cloud data in the Japanese garden, it is the visual use such as the animations. Therefore, some applications of the 3D point cloud data was investigated that are as follows. Firstly, ortho image of the Japanese garden could be outputted for the 3D point cloud data. Secondly, contour lines of the Japanese garden also could be extracted, and drawing was became possible. Consequently, drawing of Japanese garden was realized more efficiency due to achievement of laborsaving. Moreover, operation of the measurement and drawing could be performed without technical skills, and any observers can be operated. Furthermore, 3D point cloud data could be edited, and some landscape simulations that extraction and placement of tree or some objects were became possible. As a result, it can be said that the terrestrial laser scanner will be applied in landscape architecture field more widely.

Fast Intent Prediction of Multi-Cyclists in 3D Point Cloud Data using Deep Neural Networks

2021 ◽  
Author(s):  
Khaled Saleh ◽  
Ahmed Abobakr ◽  
Mohammed Hossny ◽  
Darius Nahavandi ◽  
Julie Iskander ◽  
...  
Keyword(s):  
Neural Networks ◽  
Point Cloud ◽  
Deep Neural Networks ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Intent Prediction

Automated Surface Inspection Using 3D Point Cloud Data in Manufacturing: A Case Study

2018 ◽  
Author(s):  
Romina Dastoorian ◽  
Ahmad E. Elhabashy ◽  
Wenmeng Tian ◽  
Lee J. Wells ◽  
Jaime A. Camelio
Keyword(s):  
Point Cloud ◽  
Surface Defect ◽  
Fault Location ◽  
Three Dimensional ◽  
Practical Case ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Defect Inspection ◽  
Cloud Data

With the latest advancements in three-dimensional (3D) measurement technologies, obtaining 3D point cloud data for inspection purposes in manufacturing is becoming more common. While 3D point cloud data allows for better inspection capabilities, their analysis is typically challenging. Especially with unstructured 3D point cloud data, containing coordinates at random locations, the challenges increase with higher levels of noise and larger volumes of data. Hence, the objective of this paper is to extend the previously developed Adaptive Generalized Likelihood Ratio (AGLR) approach to handle unstructured 3D point cloud data used for automated surface defect inspection in manufacturing. More specifically, the AGLR approach was implemented in a practical case study to inspect twenty-seven samples, each with a unique fault. These faults were designed to cover an array of possible faults having three different sizes, three different magnitudes, and located in three different locations. The results show that the AGLR approach can indeed differentiate between non-faulty and a varying range of faulty surfaces while being able to pinpoint the fault location. This work also serves as a validation for the previously developed AGLR approach in a practical scenario.

scholarly journals Segmentation of 3D Point Cloud Data Based on Supervoxel Technique

2020 ◽  
Vol 171 ◽  
pp. 427-435
Author(s):  
R.S. Rampriya ◽  
R. Suganya
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data
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