Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies

2016 ◽  
Vol 54 (2) ◽  
pp. 679-696 ◽  
Author(s):  
Lixia Ma ◽  
Guang Zheng ◽  
Jan U. H. Eitel ◽  
L. Monika Moskal ◽  
Wei He ◽  
...  
2021 ◽  
pp. 1-1
Author(s):  
Masamichi Oka ◽  
Ryoichi Shinkuma ◽  
Takehiro Sato ◽  
Eiji Oki ◽  
Takanori Iwai ◽  
...  

2013 ◽  
Vol 572 ◽  
pp. 155-158
Author(s):  
Hai Tao Zhu ◽  
Liang Cong

ntegrating section feature recognition with forward design is an effective method to reconstruct section curve and change feature architecture patterns from 2D to 3D. This paper proposes solutions to filter the points on the slices of point cloud data, automatically sequence the points on slices, recognize section curve feature, fit each curve segment and reconstruct section curves. All the relevant algorithms are implemented in Matlab. The point cloud data of sighting scope is used to validate the strategy. Also, Error analysis is carried out in Geomagic Studio. This strategy proves its feasibility and accuracy of completing reverse modeling process.


Author(s):  
L. Li ◽  
L. Pang ◽  
X. D. Zhang ◽  
H. Liu

Muti-baseLine SAR tomography can be used on 3D reconstruction of urban building based on SAR images acquired. In the near future, it is expected to become an important technical tool for urban multi-dimensional precision monitoring. For the moment,There is no effective method to verify the accuracy of tomographic SAR 3D point cloud of urban buildings. In this paper, a new method based on terrestrial Lidar 3D point cloud data to verify the accuracy of the tomographic SAR 3D point cloud data is proposed, 3D point cloud of two can be segmented into different facadeds. Then facet boundary extraction is carried out one by one, to evaluate the accuracy of tomographic SAR 3D point cloud of urban buildings. The experience select data of Pangu Plaza to analyze and compare, the result of experience show that the proposed method that evaluating the accuracy of tomographic SAR 3D point clou of urban building based on lidar 3D point cloud is validity and applicability


Author(s):  
D. Y. Shin ◽  
J. S. Sim ◽  
K. S. Lee

<p><strong>Abstract.</strong> A collapse of slope is one of the natural disasters that often occur during the early spring and the rainy season. In order to prevent this kind of disaster, safety monitoring is carried out through risk assessment. This assessment consists of various parameters such as inclination angle and height of the slope, and inspectors evaluate the score using the compass, the laser range finder, and so on. This approach is, however, consumed a lot of the manpower and the time. This study, therefore, aims to evaluate the rapid and accurate steep slope risk by using a terrestrial LiDAR which takes 3 dimensional spatial information data. 3D spatial information data was acquired using the terrestrial LiDAR for steep slopes classified as very unstable slopes. Noise and vegetation of the acquired scan data were removed to generate point cloud data with a rock or mountain model without vegetation. The RMSE of the registration accuracy was 0.0156 m. From the point cloud data, the inclination angle, height, shape, valley, collapse and loss were evaluated. As a result, various risk assessment parameters can be checked at once. In addition, it is expected to be used as basic data for constructing steep slope DB, providing visualization data, and time series analysis in the future.</p>


2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Yu Jin ◽  
Harry Pierson ◽  
Haitao Liao

Abstract Additive manufacturing (AM) has the unprecedented ability to create customized, complex, and nonparametric geometry, and it has made this ability accessible to individuals outside of traditional production environments. Geometric inspection technology, however, has yet to adapt to take full advantage of AM’s abilities. Coordinate measuring machines are accurate, but they are also slow, expensive to operate, and inaccessible to many AM users. On the other hand, 3D-scanners provide fast, high-density measurements, but there is a lack of feature-based analysis techniques for point cloud data. There exists a need for developing fast, feature-based geometric inspection techniques that can be implemented by users without specialized training in inspection according to geometric dimensioning and tolerancing conventions. This research proposes a new scale- and pose-invariant quality inspection method based on a novel location-orientation-shape (LOS) distribution derived from point cloud data. The key technique of the new method is to describe the shape and pose of key features via kernel density estimation and detect nonconformities based on statistical divergence. Numerical examples are provided and tests on physical AM builds are conducted to validate the method. The results show that the proposed inspection scheme is able to identify form, position, and orientation defects. The results also demonstrate how datum features can be incorporated into point cloud inspection, that datum features can be complex, nonparametric surfaces, and how the specification of datums can be more intuitive and meaningful, particularly for users without special training.


Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 317
Author(s):  
Mehrdad Eslami ◽  
Mohammad Saadatseresht

Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach.


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