scholarly journals Extreme Feature Regions Detection and Accurate Quality Assessment for Point-Cloud 3D Reconstruction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37757-37769 ◽  
Author(s):  
Yunbo Rao ◽  
Baijiang Fan ◽  
Qifei Wang ◽  
Jiansu Pu ◽  
Xun Luo ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Quality Assessment ◽  
Point Cloud

scholarly journals QUALITY ASSESSMENT OF 3D RECONSTRUCTION USING FISHEYE AND PERSPECTIVE SENSORS

2015 ◽  
Vol II-3/W4 ◽  
pp. 215-222 ◽  
Author(s):  
C. Strecha ◽  
R. Zoller ◽  
S. Rutishauser ◽  
B. Brot ◽  
K. Schneider-Zapp ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Quality Assessment ◽  
Unmanned Aerial Vehicles ◽  
Point Cloud ◽  
Laser Scanner ◽  
3D Modelling ◽  
Sensor Selection ◽  
Aerial Vehicles ◽  
Photogrammetric Method ◽  
Fisheye Lenses

Recent mathematical advances, growing alongside the use of unmanned aerial vehicles, have not only overcome the restriction of roll and pitch angles during flight but also enabled us to apply non-metric cameras in photogrammetric method, providing more flexibility for sensor selection. Fisheye cameras, for example, advantageously provide images with wide coverage; however, these images are extremely distorted and their non-uniform resolutions make them more difficult to use for mapping or terrestrial 3D modelling. In this paper, we compare the usability of different camera-lens combinations, using the complete workflow implemented in Pix4Dmapper to achieve the final terrestrial reconstruction result of a well-known historical site in Switzerland: the Chillon Castle. We assess the accuracy of the outcome acquired by consumer cameras with perspective and fisheye lenses, comparing the results to a laser scanner point cloud.

3D reconstruction methods and quality assessment for visual inspection of welds

2017 ◽  
Vol 79 ◽  
pp. 49-58 ◽  
Author(s):  
P. Rodríguez-Gonzálvez ◽  
M. Rodríguez-Martín ◽  
Luís F. Ramos ◽  
D. González-Aguilera
Keyword(s):  
3D Reconstruction ◽  
Quality Assessment ◽  
Visual Inspection ◽  
Reconstruction Methods

scholarly journals ASSESSMENT OF THE QUALITY OF DIGITAL TERRAIN MODEL PRODUCED FROM UNMANNED AERIAL SYSTEM IMAGERY

2016 ◽  
Vol XLI-B1 ◽  
pp. 893-899
Author(s):  
M. Kosmatin Fras ◽  
A. Kerin ◽  
M. Mesarič ◽  
V. Peterman ◽  
D. Grigillo
Keyword(s):  
Quality Assessment ◽  
Point Cloud ◽  
Digital Terrain Model ◽  
Quality Parameter ◽  
Unmanned Aerial System ◽  
Test Field ◽  
Absolute Deviation ◽  
Terrain Model ◽  
Digital Terrain

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

3D Reconstruction Using Multiple View Stereo and a Brief Introduction to Kinect

2014 ◽  
pp. 15-37
Author(s):  
Brojeshwar Bhowmick
Keyword(s):  
3D Reconstruction ◽  
Projective Geometry ◽  
Point Cloud ◽  
Microsoft Kinect ◽  
Sparse Reconstruction ◽  
Basic Properties ◽  
Multiple View ◽  
The Subject ◽  
Dense Reconstruction ◽  
3D Information

This chapter deals with the methodology of 3D reconstruction, both sparse and dense. The basic properties of the projective geometry and the camera models are introduced to understand the preliminaries about the subject. A more detail can be found in the book (Hartley & Zisserman, 2000). The sparse reconstruction deals with reconstructing 3D points for few image points. There are gaps in the reconstructed 3D points. Dense reconstruction tries to fill up gaps and make the density of the reconstruction higher. Estimation of correspondences is an integral part of multiview reconstruction and the author will discuss the point correspondences among images here. Finally the author will introduce the Microsoft Kinect, a divice which directly capture 3D information in realtime, and will show how to enhance the Kinect point cloud using vision framework.

Analysis of Aero-engine Blade Surface Laser Point Cloud 3D Reconstruction Error

2018 ◽  
Vol 47 (5) ◽  
pp. 512002
Author(s):  
孙彬 SUN Bin ◽  
杜虎兵 DU Hu-bing ◽  
王建华 WANG Jian-hua ◽  
李兵 LI Bing
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Reconstruction Error ◽  
Blade Surface ◽  
Aero Engine ◽  
Engine Blade ◽  
Surface Laser

3D Reconstruction Approach for Outdoor Scene Based on Multiple Point Cloud Fusion

2019 ◽  
Vol 47 (10) ◽  
pp. 1761-1772 ◽  
Author(s):  
Hui Chen ◽  
Yan Feng ◽  
Jian Yang ◽  
Chenggang Cui
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Multiple Point

scholarly journals Quantification Method for the Uncertainty of Matching Point Distribution on 3D Reconstruction

2020 ◽  
Vol 9 (4) ◽  
pp. 187
Author(s):  
Yuxia Bian ◽  
Xuejun Liu ◽  
Meizhen Wang ◽  
Hongji Liu ◽  
Shuhong Fang ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Point Distribution ◽  
Multiple Images ◽  
Quantification Method ◽  
Real Scene ◽  
Direct Data ◽  
The Relationship ◽  
Overlapping Region

Matching points are the direct data sources of the fundamental matrix, camera parameters, and point cloud calculation. Thus, their uncertainty has a direct influence on the quality of image-based 3D reconstruction and is dependent on the number, accuracy, and distribution of the matching points. This study mainly focuses on the uncertainty of matching point distribution. First, horizontal dilution of precision (HDOP) is used to quantify the feature point distribution in the overlapping region of multiple images. Then, the quantization method is constructed. H D O P ∗ ¯ , the average of 2 × arctan ( H D O P × n 5 − 1 ) / π on all images, is utilized to measure the uncertainty of matching point distribution on 3D reconstruction. Finally, simulated and real scene experiments were performed to describe and verify the rationality of the proposed method. We found that the relationship between H D O P ∗ ¯ and the matching point distribution in this study was consistent with that between matching point distribution and 3D reconstruction. Consequently, it may be a feasible method to predict the quality of 3D reconstruction by calculating the uncertainty of matching point distribution.

Point Cloud Noise and Outlier Removal for Image-Based 3D Reconstruction

2016 ◽  
Author(s):  
Katja Wolff ◽  
Changil Kim ◽  
Henning Zimmer ◽  
Christopher Schroers ◽  
Mario Botsch ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Outlier Removal
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