Feature Point Cloud Based Registration in Augmented Reality

2021 ◽  
pp. 418-427
Author(s):  
Konrad Koniarski ◽  
Andrzej Myśliński
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Feature Point

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 Incremental 3D Cuboid Modeling with Drift Compensation

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 178 ◽  
Author(s):  
Masashi Mishima ◽  
Hideaki Uchiyama ◽  
Diego Thomas ◽  
Rin-ichiro Taniguchi ◽  
Rafael Roberto ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Detection Method ◽  
False Detection ◽  
Cluttered Environments ◽  
Plane Map ◽  
Localization And Mapping ◽  
Cad Models ◽  
Drift Compensation ◽  
Sequential Measurements

This paper presents a framework of incremental 3D cuboid modeling by using the mapping results of an RGB-D camera based simultaneous localization and mapping (SLAM) system. This framework is useful in accurately creating cuboid CAD models from a point cloud in an online manner. While performing the RGB-D SLAM, planes are incrementally reconstructed from a point cloud in each frame to create a plane map. Then, cuboids are detected in the plane map by analyzing the positional relationships between the planes, such as orthogonality, convexity, and proximity. Finally, the position, pose, and size of a cuboid are determined by computing the intersection of three perpendicular planes. To suppress the false detection of the cuboids, the cuboid shapes are incrementally updated with sequential measurements to check the uncertainty of the cuboids. In addition, the drift error of the SLAM is compensated by the registration of the cuboids. As an application of our framework, an augmented reality-based interactive cuboid modeling system was developed. In the evaluation at cluttered environments, the precision and recall of the cuboid detection were investigated, compared with a batch-based cuboid detection method, so that the advantages of our proposed method were clarified.

scholarly journals An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC)

2020 ◽  
Vol 9 ◽  
Author(s):  
D. Graziosi ◽  
O. Nakagami ◽  
S. Kuma ◽  
A. Zaghetto ◽  
T. Suzuki ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Cultural Heritage ◽  
Motion Picture ◽  
Point Cloud ◽  
Autonomous Driving ◽  
Data Representation ◽  
Media Content ◽  
International Standard ◽  
3D Data ◽  
Immersive Media

Abstract This article presents an overview of the recent standardization activities for point cloud compression (PCC). A point cloud is a 3D data representation used in diverse applications associated with immersive media including virtual/augmented reality, immersive telepresence, autonomous driving and cultural heritage archival. The international standard body for media compression, also known as the Motion Picture Experts Group (MPEG), is planning to release in 2020 two PCC standard specifications: video-based PCC (V-CC) and geometry-based PCC (G-PCC). V-PCC and G-PCC will be part of the ISO/IEC 23090 series on the coded representation of immersive media content. In this paper, we provide a detailed description of both codec algorithms and their coding performances. Moreover, we will also discuss certain unique aspects of point cloud compression.

scholarly journals ARTS, an AR Tourism System, for the Integration of 3D Scanning and Smartphone AR in Cultural Heritage Tourism and Pedagogy

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3725 ◽  
Author(s):  
Naai-Jung Shih ◽  
Pei-Huang Diao ◽  
Yi Chen
Keyword(s):  
Augmented Reality ◽  
Cultural Heritage ◽  
Point Cloud ◽  
Heritage Tourism ◽  
Urban Fabric ◽  
Heritage Site ◽  
System Usability ◽  
3D Scans ◽  
Graphic Module ◽  
Cultural Heritage Tourism

Interactions between cultural heritage, tourism, and pedagogy deserve investigation in an as-built environment under a macro- or micro-perspective of urban fabric. The heritage site of Shih Yih Hall, Lukang, was explored. An Augmented Reality Tourism System (ARTS) was developed on a smartphone-based platform for a novel application scenario using 3D scans converted from a point cloud to a portable interaction size. ARTS comprises a real-time environment viewing module, a space-switching module, and an Augmented Reality (AR) guide graphic module. The system facilitates scenario initiations, projection and superimposition, annotation, and interface customization, with software tools developed using ARKit® on the iPhone XS Max®. The three-way interaction between urban fabric, cultural heritage tourism, and pedagogy was made possible through background block-outs and an additive or selective display. The illustration of the full-scale experience of the smartphone app was made feasible for co-relating the cultural dependence of urban fabric on tourism. The great fidelity of 3D scans and AR scenes act as a pedagogical aid for students or tourists. A Post-Study System Usability Questionnaire (PSSUQ) evaluation verified the usefulness of ARTS.

scholarly journals Augmented reality system using lidar point cloud data for displaying dimensional information of objects on mobile phones

2014 ◽  
Vol II-5 ◽  
pp. 153-159 ◽  
Author(s):  
S. Gupta ◽  
B. Lohani
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Smart Phone ◽  
Research Problem ◽  
Point Cloud Data ◽  
Registration Method ◽  
Cloud Data ◽  
Augmented Reality System ◽  
The Status ◽  
Intensity Image

Mobile augmented reality system is the next generation technology to visualise 3D real world intelligently. The technology is expanding at a fast pace to upgrade the status of a smart phone to an intelligent device. The research problem identified and presented in the current work is to view actual dimensions of various objects that are captured by a smart phone in real time. The methodology proposed first establishes correspondence between LiDAR point cloud, that are stored in a server, and the image t hat is captured by a mobile. This correspondence is established using the exterior and interior orientation parameters of the mobile camera and the coordinates of LiDAR data points which lie in the viewshed of the mobile camera. A pseudo intensity image is generated using LiDAR points and their intensity. Mobile image and pseudo intensity image are then registered using image registration method SIFT thereby generating a pipeline to locate a point in point cloud corresponding to a point (pixel) on the mobile image. The second part of the method uses point cloud data for computing dimensional information corresponding to the pairs of points selected on mobile image and fetch the dimensions on top of the image. This paper describes all steps of the proposed method. The paper uses an experimental setup to mimic the mobile phone and server system and presents some initial but encouraging results

scholarly journals A 3D Tracking and Registration Method Based on Point Cloud and Visual Features for Augmented Reality Aided Assembly System

2019 ◽  
Vol 37 (1) ◽  
pp. 143-151 ◽  
Author(s):  
Yue Wang ◽  
Shusheng Zhang ◽  
Xiaoliang Bai
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Feature Matching ◽  
Experimental Result ◽  
Assembly System ◽  
Visual Features ◽  
Depth Information ◽  
Visual Feature ◽  
3D Tracking ◽  
Registration Process

To improve the robustness and applicability of 3D tracking and registration for augmented reality(AR) aided mechanical assembly system, a 3D registration and tracking method based on the point cloud and visual features is proposed. Firstly, the reference model point cloud is used to definite absolute tracking coordinate system, thus the locating datum of the virtual assembly guidance information is determined. Then by adding visual features matching to the iterative closest points (ICP) registration process, the robustness of tracking and registration is improved. In order to obtain sufficient number of visual feature matching points in this process, a visual feature matching strategy based on orientation vector consistency is proposed. Finally, the loop closure detection and global pose optimization from key frames are added in the tracking registration process. The experimental result shows that the proposed method has good real-time performance and accuracy, and the running speed can reach 30 frames per second. Moreover, it also shows good robustness when the camera is moving fast and the depth information is inaccurate, and the comprehensive performance of the proposed method is better than the KinectFusion method.

scholarly journals Anisotropic neighborhood searching for point cloud with sharp feature

2020 ◽  
pp. 002029402096424
Author(s):  
Xiaocui Yuan ◽  
Baoling Liu ◽  
Yongli Ma
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Accurate Result ◽  
Noise Removal ◽  
Feature Point ◽  
Normal Vector ◽  
Feature Points ◽  
Normal Estimation ◽  
Sharp Feature ◽  
Anisotropic Neighborhood

The k-nearest neighborhoods (kNN) of feature points of complex surface model are usually isotropic, which may lead to sharp feature blurring during data processing, such as noise removal and surface reconstruction. To address this issue, a new method was proposed to search the anisotropic neighborhood for point cloud with sharp feature. Constructing KD tree and calculating kNN for point cloud data, the principal component analysis method was employed to detect feature points and estimate normal vectors of points. Moreover, improved bilateral normal filter was used to refine the normal vector of feature point to obtain more accurate normal vector. The isotropic kNN of feature point were segmented by mapping the kNN into Gaussian sphere to form different data-clusters, with the hierarchical clustering method used to separate the data in Gaussian sphere into different clusters. The optimal anisotropic neighborhoods of feature point corresponded to the cluster data with the maximum point number. To validate the effectiveness of our method, the anisotropic neighbors are applied to point data processing, such as normal estimation and point cloud denoising. Experimental results demonstrate that the proposed algorithm in the work is more time-consuming, but provides a more accurate result for point cloud processing by comparing with other kNN searching methods. The anisotropic neighborhood searched by our method can be used to normal estimation, denoising, surface fitting and reconstruction et al. for point cloud with sharp feature, and our method can provide more accurate result comparing with isotropic neighborhood.

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