Multi-resolution 3D reconstruction of cultural landscape heritage based on cloud computing and hd image data

2020 ◽  
Vol 39 (4) ◽  
pp. 5097-5107
Author(s):  
Long Zhang ◽  
Leyi Liu ◽  
Bailong Chai ◽  
Man Xu ◽  
Yuhong Song

Cultural landscapes are cultural property and they are an illustration of the evolution of human society and the living environment over time. As cultural landscape is being valued more and more, the use of 3D modeling is becoming more and more important. As for the 3D reconstruction technology, most of the current methods are complicated in terms of network construction, use, and storage, and then affect the reconstruction efficiency of subsequent cultural landscape heritage. To obtain the 3D reconstruction technology with high reconstruction efficiency, this paper combines the circumferential binary feature extraction algorithm and cloud computing technology, and proposes a circumferential binary feature extraction and matching search method. The interior-point rate of the CBD algorithm in this paper is greater than 72%, which is higher than the interior point rate of other different algorithms, which indicates that the CBD algorithm in this paper is suitable for matching HD rotated images. The experimental results show that the circular binary features extracted by the article have strong adaptability and fast contrast rate. To better the 3D reconstruction of cultural landscape heritage in the later period, this paper also improves the 4PSC point cloud rough registration algorithm. The experimental results show that compared with other coarse registration algorithms, the improved point cloud coarse registration algorithm improves the registration accuracy and the registration effect is good, which proves the effectiveness of the algorithm.

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4860
Author(s):  
Zichao Shu ◽  
Songxiao Cao ◽  
Qing Jiang ◽  
Zhipeng Xu ◽  
Jianbin Tang ◽  
...  

In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.


2015 ◽  
Vol 42 (3) ◽  
pp. 0308002 ◽  
Author(s):  
黄源 Huang Yuan ◽  
达飞鹏 Da Feipeng ◽  
陶海跻 Tao Haiji

2020 ◽  
Vol 57 (4) ◽  
pp. 041510
Author(s):  
王鹏 Wang Peng ◽  
朱睿哲 Zhu Ruizhe ◽  
孙长库 Sun Changku

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yongshan Liu ◽  
Dehan Kong ◽  
Dandan Zhao ◽  
Xiang Gong ◽  
Guichun Han

The existing registration algorithms suffer from low precision and slow speed when registering a large amount of point cloud data. In this paper, we propose a point cloud registration algorithm based on feature extraction and matching; the algorithm helps alleviate problems of precision and speed. In the rough registration stage, the algorithm extracts feature points based on the judgment of retention points and bumps, which improves the speed of feature point extraction. In the registration process, FPFH features and Hausdorff distance are used to search for corresponding point pairs, and the RANSAC algorithm is used to eliminate incorrect point pairs, thereby improving the accuracy of the corresponding relationship. In the precise registration phase, the algorithm uses an improved normal distribution transformation (INDT) algorithm. Experimental results show that given a large amount of point cloud data, this algorithm has advantages in both time and precision.


2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  

Author(s):  
Mahdi Saleh ◽  
Shervin Dehghani ◽  
Benjamin Busam ◽  
Nassir Navab ◽  
Federico Tombari

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