scholarly journals Large-scale electroholography by HORN-8 from a point-cloud model with 400,000 points

2018 ◽  
Vol 26 (26) ◽  
pp. 34259 ◽  
Author(s):  
Yota Yamamoto ◽  
Hirotaka Nakayama ◽  
Naoki Takada ◽  
Takashi Nishitsuji ◽  
Takashige Sugie ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Point Cloud Model

scholarly journals GEOREFERENCING AND REPROJECTION ERROR INVESTIGATION ON IMAGE BASED 3D DIGITIZATION AND MAPPING OF HISTORICAL BUILDINGS

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 71-75
Author(s):  
C. Altuntas
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Low Cost ◽  
Three Dimensional ◽  
Control Points ◽  
Historical Buildings ◽  
Reprojection Error ◽  
Dense Point ◽  
Point Cloud Model

<p><strong>Abstract.</strong> Image based dense point cloud creation is easy and low-cost application for three dimensional digitization of small and large scale objects and surfaces. It is especially attractive method for cultural heritage documentation. Reprojection error on conjugate keypoints indicates accuracy of the model and keypoint localisation in this method. In addition, sequential registration of the images from large scale historical buildings creates big cumulative registration error. Thus, accuracy of the model should be increased with the control points or loop close imaging. The registration of point point cloud model into the georeference system is performed using control points. In this study historical Sultan Selim Mosque that was built in sixteen century by Great Architect Sinan was modelled via photogrammetric dense point cloud. The reprojection error and number of keypoints were evaluated for different base/length ratio. In addition, georeferencing accuracy was evaluated with many configuration of control points with loop and without loop closure imaging.</p>

Interactive Pickup of Three-dimensional Fine Point Cloud Based on GPU

2014 ◽  
Vol 8 (1) ◽  
pp. 631-635
Author(s):  
Ming Huang ◽  
Fang Yang ◽  
Yong Zhang ◽  
Xinle Fu
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Three Dimensional ◽  
Spatial Transformation ◽  
Three Dimensional Model ◽  
Cloud Data ◽  
Speed Advantage ◽  
Point Cloud Model ◽  
Screen Space

Three-dimensional fine point cloud has gradually become a key data source of three-dimensional model. The large scale point cloud interactive quick pick up is a kind of important operation in the point cloud data processing and applications. Since the point cloud model is composed of massive points, the speed of ordinary picking method is limited. A GPU-based point cloud picking algorithm was thus presented to solve the problem. The basic idea of the algorithm is that by spatial transformation converting the point cloud to screen space, and then, the point was calculated which is the nearest to the mouse click point in screen space. The GPU's parallel computing capabilities were used to achieve spatial transformation and distance comparison by compute shader in this algorithm. So the speed of the pickup has been increased. The results show that compared with the CPU, the pickup method based on GPU has greater speed advantage. Especially for the point cloud over 4 million points, the speed of the pickup has been increased 2-3 times faster.

scholarly journals VISUALIZATION OF POINT CLOUD MODELS IN MOBILE AUGMENTED REALITY USING CONTINUOUS LEVEL OF DETAIL METHOD

2020 ◽  
Vol XLIV-4/W1-2020 ◽  
pp. 167-170
Author(s):  
L. Zhang ◽  
P. van Oosterom ◽  
H. Liu
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Cloud Model ◽  
Point Clouds ◽  
Level Of Detail ◽  
Mobile Augmented Reality ◽  
Cloud Models ◽  
Point Cloud Model ◽  
Visual Artifacts ◽  
Mobile Ar

Abstract. Point clouds have become one of the most popular sources of data in geospatial fields due to their availability and flexibility. However, because of the large amount of data and the limited resources of mobile devices, the use of point clouds in mobile Augmented Reality applications is still quite limited. Many current mobile AR applications of point clouds lack fluent interactions with users. In our paper, a cLoD (continuous level-of-detail) method is introduced to filter the number of points to be rendered considerably, together with an adaptive point size rendering strategy, thus improve the rendering performance and remove visual artifacts of mobile AR point cloud applications. Our method uses a cLoD model that has an ideal distribution over LoDs, with which can remove unnecessary points without sudden changes in density as present in the commonly used discrete level-of-detail approaches. Besides, camera position, orientation and distance from the camera to point cloud model is taken into consideration as well. With our method, good interactive visualization of point clouds can be realized in the mobile AR environment, with both nice visual quality and proper resource consumption.

scholarly journals Research on the Detection Method of Implicit Self Symmetry in a High-Level Semantic Model

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 28 ◽  
Author(s):  
Chao Wang
Keyword(s):  
Point Cloud ◽  
Detection Method ◽  
Cloud Model ◽  
High Reliability ◽  
Semantic Model ◽  
Symmetry Detection ◽  
Model Surface ◽  
Point Cloud Model ◽  
High Level ◽  
Skeleton Point

In order to improve the accuracy of semantic model intrinsic detection, a skeleton-based high-level semantic model intrinsic self-symmetry detection method is proposed. The semantic analysis of the model set is realized by the uniform segmentation of the model within the same style, the component correspondence of the model between different styles, and the shape content clustering. Based on the results of clustering analysis, for a given three-dimensional (3D) point cloud model, according to the curve skeleton, the skeleton point pairs reflecting the symmetry between the model surface points are obtained by the election method, and the symmetry is extended to the model surface vertices according to these skeleton point pairs. With the help of skeleton, the symmetry of the point cloud model is obtained, and then the symmetry region of point cloud model is obtained by the symmetric correspondence matrix and spectrum method, so as to realize the intrinsic symmetry detection of the model. The experimental results show that the proposed method has the advantages of less time, high accuracy, and high reliability.

scholarly journals Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 774 ◽  
Author(s):  
Sun ◽  
Wang ◽  
Ding ◽  
Lu ◽  
Sun
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Imaging System ◽  
Cloud Model ◽  
Tree Canopy ◽  
Coefficient Of Determination ◽  
Remote Measurement ◽  
Probability Density Estimation ◽  
Point Cloud Model ◽  
Aerial Vehicle

Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based on the probability density estimation and rapid segmentation of the point-cloud data for each apple tree, through which the tree canopy height, H, width, W, and volume, V, were determined for remote orchard canopy evaluation. When the ground sampling distance (GSD) was in the range of 2.13 to 6.69 cm/px, the orchard point-cloud model had a measurement accuracy of 100.00% for the rows and 90.86% to 98.20% for the columns. The coefficient of determination, R2, was in the range of 0.8497 to 0.9376, 0.8103 to 0.9492, and 0.8032 to 0.9148, respectively, and the average relative error was in the range of 1.72% to 3.42%, 2.18% to 4.92%, and 7.90% to 13.69%, respectively, among the H, W, and V values measured manually and by UAV photogrammetry. The results showed that UAV visual imaging is suitable for 3D morphological remote canopy evaluations, facilitates orchard canopy informatization, and contributes substantially to efficient management and control of modern standard orchards.

scholarly journals Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3345 ◽  
Author(s):  
Guoxiang Sun ◽  
Xiaochan Wang ◽  
Ye Sun ◽  
Yongqian Ding ◽  
Wei Lu
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Three Dimensional ◽  
Measurement Model ◽  
View Point ◽  
Nondestructive Measurement ◽  
Tomato Plants ◽  
Single View ◽  
Greenhouse Tomato ◽  
Point Cloud Model

Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.

Quality evaluation of point cloud model for interior structure of a common building

2017 ◽  
Author(s):  
Gen Li ◽  
Yiping Chen ◽  
Chenglu Wen ◽  
Cheng Wang ◽  
Jonathan Li ◽  
...  
Keyword(s):  
Point Cloud ◽  
Quality Evaluation ◽  
Cloud Model ◽  
Interior Structure ◽  
Point Cloud Model

3D point cloud model construction for industrial parts with few shape features

2016 ◽  
Vol 2016 (0) ◽  
pp. 2A1-19a4
Author(s):  
Yuto TERASHIMA ◽  
Shogo ARAI ◽  
Koichi HASHIMOTO
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Model Construction ◽  
3D Point Cloud ◽  
Shape Features ◽  
Point Cloud Model
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