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

<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>

Download Full-text

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

The Open Electrical & Electronic Engineering Journal ◽

10.2174/1874129001408010631 ◽

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.

Download Full-text

Automatic pipe and elbow recognition from three-dimensional point cloud model of industrial plant piping system using convolutional neural network-based primitive classification

Automation in Construction ◽

10.1016/j.autcon.2020.103236 ◽

2020 ◽

Vol 116 ◽

pp. 103236

Author(s):

Youngdoo Kim ◽

Cong Hong Phong Nguyen ◽

Young Choi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Point Cloud ◽

Cloud Model ◽

Three Dimensional ◽

Industrial Plant ◽

Piping System ◽

Point Cloud Model

Download Full-text

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

Sensors ◽

10.3390/s19153345 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3345 ◽

Cited By ~ 2

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.

Download Full-text

Three Dimensional Modeling of Buildings Using Three Dimensional Laser Scanning Data

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.594-597.2398 ◽

2012 ◽

Vol 594-597 ◽

pp. 2398-2401

Author(s):

Dong Ling Ma ◽

Jian Cui ◽

Fei Cai

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

3D Visualization ◽

Cloud Model ◽

Three Dimensional ◽

3D Models ◽

Three Dimensional Modeling ◽

Dimensional Modeling ◽

Feature Lines ◽

Point Cloud Model

This paper provides a scheme to construct three dimensional (3D) model fast using laser scanning data. In the approach, firstly, laser point cloud are scanned from different scan positions and the point cloud coming from neighbor scan stations are spliced automatically to combine a uniform point cloud model, and then feature lines are extracted through the point cloud, and the framework of the building are extracted to generate 3D models. At last, a conclusion can be drawn that 3D visualization model can be generated quickly using 3D laser scanning technology. The experiment result shows that it will bring the application model and technical advantage which traditional mapping way can not have.

Download Full-text

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

Optics Express ◽

10.1364/oe.26.034259 ◽

2018 ◽

Vol 26 (26) ◽

pp. 34259 ◽

Cited By ~ 15

Author(s):

Yota Yamamoto ◽

Hirotaka Nakayama ◽

Naoki Takada ◽

Takashi Nishitsuji ◽

Takashige Sugie ◽

...

Keyword(s):

Point Cloud ◽

Large Scale ◽

Cloud Model ◽

Point Cloud Model

Download Full-text

A novel watermarking algorithm for three-dimensional point-cloud models based on vertex curvature

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719826042 ◽

2019 ◽

Vol 15 (1) ◽

pp. 155014771982604 ◽

Cited By ~ 2

Author(s):

Jing Liu ◽

Yajie Yang ◽

Douli Ma ◽

Wenjuan He ◽

Yinghui Wang

Keyword(s):

Root Mean Square ◽

Point Cloud ◽

Cloud Model ◽

Three Dimensional ◽

Mean Square ◽

Geometric Attack ◽

Cloud Models ◽

Watermark Embedding ◽

Point Cloud Model ◽

Watermarking Scheme

A new blind watermarking scheme for three-dimensional point-cloud models is proposed based on vertex curvature to achieve an appropriate trade-off between transparency and robustness. The root mean square curvature of local set of every vertex is first calculated for the three-dimensional point-cloud model and then the vertices with larger root mean square curvature are used to carry the watermarking information; the vertices with smaller root mean square curvature are exploited to establish the synchronization relation between the watermark embedding and extraction. The three-dimensional point-cloud model is divided into ball rings, and the watermarking information is inserted by modifying the radial radii of vertices within ball rings. Those vertices taking part in establishing the synchronization relation do not carry the watermarking information; therefore, the synchronization relation is not affected by the embedded watermark. Experimental results show the proposed method outperforms other well-known three-dimensional point-cloud model watermarking methods in terms of imperceptibility and robustness, especially for against geometric attack.

Download Full-text

Reconstruction of measurable three-dimensional point cloud model based on large-scene archaeological excavation sites

Journal of Electronic Imaging ◽

10.1117/1.jei.26.1.011027 ◽

2017 ◽

Vol 26 (1) ◽

pp. 011027 ◽

Cited By ~ 1

Author(s):

Chun-Sen Zhang ◽

Meng-Meng Zhang ◽

Wei-Xing Zhang

Keyword(s):

Point Cloud ◽

Cloud Model ◽

Three Dimensional ◽

Archaeological Excavation ◽

Model Based ◽

Point Cloud Model

Download Full-text

Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110656 ◽

2020 ◽

Vol 9 (11) ◽

pp. 656

Author(s):

Muhammad Hamid Chaudhry ◽

Anuar Ahmad ◽

Qudsia Gulzar

Keyword(s):

Point Cloud ◽

Large Scale ◽

Accuracy Assessment ◽

Computational Cost ◽

Three Dimensional ◽

Point Clouds ◽

Ground Control ◽

Control Points ◽

Ground Control Points ◽

High Computational Cost

Unmanned Aerial Vehicles (UAVs) as a surveying tool are mainly characterized by a large amount of data and high computational cost. This research investigates the use of a small amount of data with less computational cost for more accurate three-dimensional (3D) photogrammetric products by manipulating UAV surveying parameters such as flight lines pattern and image overlap percentages. Sixteen photogrammetric projects with perpendicular flight plans and a variation of 55% to 85% side and forward overlap were processed in Pix4DMapper. For UAV data georeferencing and accuracy assessment, 10 Ground Control Points (GCPs) and 18 Check Points (CPs) were used. Comparative analysis was done by incorporating the median of tie points, the number of 3D point cloud, horizontal/vertical Root Mean Square Error (RMSE), and large-scale topographic variations. The results show that an increased forward overlap also increases the median of the tie points, and an increase in both side and forward overlap results in the increased number of point clouds. The horizontal accuracy of 16 projects varies from ±0.13m to ±0.17m whereas the vertical accuracy varies from ± 0.09 m to ± 0.32 m. However, the lowest vertical RMSE value was not for highest overlap percentage. The tradeoff among UAV surveying parameters can result in high accuracy products with less computational cost.

Download Full-text