3D point cloud model reconstruction method by using multi–view 2D images and 3D point clouds

2018 ◽  
Vol 2018 (0) ◽  
pp. 2A1-I17
Author(s):  
Yukihiro TODA ◽  
Koichi HASHIMOTO
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Point Clouds ◽  
Reconstruction Method ◽  
3D Point Cloud ◽  
Model Reconstruction ◽  
3D Point Clouds ◽  
Point Cloud Model ◽  
2D Images

scholarly journals Hole Repairing Algorithm for 3D Point Cloud Model of Symmetrical Objects Grasped by the Manipulator

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7558
Author(s):  
Linyan Cui ◽  
Guolong Zhang ◽  
Jinshen Wang
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Engineering Application ◽  
Point Clouds ◽  
Real Point ◽  
3D Point Cloud ◽  
Virtual Camera ◽  
3D Point Clouds ◽  
Cloud Models ◽  
Point Cloud Model

For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.

scholarly journals MULTI-SENSOR 3D RECORDING PIPELINE FOR THE DOCUMENTATION OF JAVANESE TEMPLES

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 829-834
Author(s):  
A. Murtiyos ◽  
P. Grussenmeyer ◽  
D. Suwardhi ◽  
W. A. Fadilah ◽  
H. A. Permana ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Cloud Model ◽  
Point Clouds ◽  
Heritage Sites ◽  
Advantages And Disadvantages ◽  
3D Point Clouds ◽  
Point Cloud Model ◽  
Geometric Quality ◽  
Different Characteristics

<p><strong>Abstract.</strong> 3D recording is an important procedure in the conservation of heritage sites. This past decade, a myriad of 3D sensors has appeared in the market with different advantages and disadvantages. Most notably, the laser scanning and photogrammetry methods have become some of the most used techniques in 3D recording. The integration of these different sensors is an interesting topic, one which will be discussed in this paper. Integration is an activity to combine two or more data with different characteristics to produce a 3D model with the best results. The discussion in this study includes the process of acquisition, processing, and analysis of the geometric quality from the results of the 3D recording process; starting with the acquisition method, registration and georeferencing process, up to the integration of laser scanning and photogrammetry 3D point clouds. The final result of the integration of the two point clouds is the 3D point cloud model that has become a single entity. Some detailed parts of the object of interest draw both geometric and textural information from photogrammetry, while laser scanning provided a point cloud depicting the overall overview of the building. The object used as our case study is Sari Temple, located in Special Region of Yogyakarta, Indonesia.</p>

scholarly journals Optimum Assembly Planning for Modular Construction Using BIM and 3D Point Clouds

2016 ◽  
Author(s):  
Mohammad Nahangi ◽  
Christopher Rausch ◽  
Carl Haas
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Point Clouds ◽  
Assembly Planning ◽  
Modular Construction ◽  
Optimization Framework ◽  
3D Point Clouds ◽  
Information Models ◽  
Building Information ◽  
Point Cloud Model

Geometric and dimensional deviations often create challenges for component aggregation in the assembly of interchangeable components in modular construction. Although the components are designed interchangeably, once they are fabricated, there are inevitable discrepancies between the designed and built states. Such discrepancies create problems for fitting interchangeable modular components. This paper presents a framework for optimally planning the assembly of interchangeable components based on their as-built state. A 3D point cloud model is captured and the critical interfaces between modules are compared to the original state, integrated in the building information models (BIM), as 3D drawings. The optimization framework is implemented based on two different approaches: (1) minimization of the total deviation for minimizing rework, and (2) intervention of rework by finding the best matching component for each investigated slot. Results show that the method can be effectively used for reducing rework in modular construction by optimum assembly planning.

scholarly journals COMPARATIVE ANALYSIS OF 3D POINT CLOUDS GENERATED FROM A FREEWARE AND TERRESTRIAL LASER SCANNER

2017 ◽  
Vol XLII-4/W2 ◽  
pp. 67-71
Author(s):  
K. R. Dayal ◽  
S. Raghavendra ◽  
H. Pande ◽  
P. S. Tiwari ◽  
I. Chauhan
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Laser Scanner ◽  
Point Clouds ◽  
Sufficient Information ◽  
Terrestrial Laser Scanner ◽  
Great Loss ◽  
3D Point Clouds ◽  
Plane Fitting ◽  
Point Cloud Model

In the recent past, several heritage structures have faced destruction due to both human-made incidents and natural calamities that have caused a great loss to the human race regarding its cultural achievements. In this context, the importance of documenting such structures to create a substantial database cannot be emphasised enough. The Clock Tower of Dehradun, India is one such structure. There is a lack of sufficient information in the digital domain, which justified the need to carry out this study. Thus, an attempt has been made to gauge the possibilities of using open source 3D tools such as VSfM to quickly and easily obtain point clouds of an object and assess its quality. The photographs were collected using consumer grade cameras with reasonable effort to ensure overlap. The sparse reconstruction and dense reconstruction were carried out to generate a 3D point cloud model of the tower. A terrestrial laser scanner (TLS) was also used to obtain a point cloud of the tower. The point clouds obtained from the two methods were analyzed to understand the quality of the information present; TLS acquired point cloud being a benchmark to assess the VSfM point cloud. They were compared to analyze the point density and subjected to a plane-fitting test for sample flat portions on the structure. The plane-fitting test revealed the <q>planarity</q> of the point clouds. A Gauss distribution fit yielded a standard deviation of 0.002 and 0.01 for TLS and VSfM, respectively. For more insight, comparisons with Agisoft Photoscan results were also made.

scholarly journals Quality assessment of 3D point cloud of industrial buildings from imagery acquired by oblique and nadir UAV flights

2020 ◽  
pp. 131-139
Author(s):  
Cao Xuan Cuong ◽  
Le Van Canh ◽  
Pham Van Chung ◽  
Le Duc Tinh ◽  
Pham Trung Dung ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Point Cloud ◽  
3D Model ◽  
Cloud Model ◽  
Point Clouds ◽  
3D Point Cloud ◽  
Industrial Buildings ◽  
Point Cloud Model ◽  
Mean Square Errors

Purpose. The main objective of this paper is to assess the quality of the 3D model of industrial buildings generated from Unmanned Aerial Vehicle (UAV) imagery datasets, including nadir (N), oblique (O), and Nadir and Oblique (N+O) UAV datasets. Methodology. The quality of a 3D model is defined by the accuracy and density of point clouds created from UAV images. For this purpose, the UAV was deployed to acquire images with both O and N flight modes over an industrial mining area containing a mine shaft tower, factory housing and office buildings. The quality assessment was conducted for the 3D point cloud model of three main objects such as roofs, facades, and ground surfaces using CheckPoints (CPs) and terrestrial laser scanning (TLS) point clouds as the reference datasets. The Root Mean Square Errors (RMSE) were calculated using CP coordinates, and cloud to cloud distances were computed using TLS point clouds, which were used for the accuracy assessment. Findings. The results showed that the point cloud model generated by the N flight mode was the most accurate but least dense, whereas that of the O mode was the least accurate but most detailed level in comparison with the others. Also, the combination of O and N datasets takes advantages of individual mode as the point clouds accuracy is higher than that of case O, and its density is much higher than that of case N. Therefore, it is optimal to build exceptional accurate and dense point clouds of buildings. Originality. The paper provides a comparative analysis in quality of point cloud of roofs and facades generated from UAV photogrammetry for mining industrial buildings. Practical value. Findings of the study can be used as references for both UAV survey practices and applications of UAV point cloud. The paper provides useful information for making UAV flight planning, or which UAV points should be integrated into TLS points to have the best point cloud.

scholarly journals Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

Aerospace ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 94 ◽  
Author(s):  
Hriday Bavle ◽  
Jose Sanchez-Lopez ◽  
Paloma Puente ◽  
Alejandro Rodriguez-Ramos ◽  
Carlos Sampedro ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
Indoor Environments ◽  
3D Point Cloud ◽  
Flight Altitude ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Laser Altimeters ◽  
Altitude Estimation

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

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.

Sign in / Sign up

Export Citation Format

Share Document