scholarly journals Generation of Approximate 2D and 3D Floor Plans from 3D Point Clouds

2019 ◽  
Author(s):  
Vladeta Stojanovic ◽  
Matthias Trapp ◽  
Rico Richter ◽  
Jürgen Döllner
Keyword(s):  
Point Clouds ◽  
3D Point Clouds ◽  
Floor Plans ◽  
2D And 3D

scholarly journals GRAMMAR-SUPPORTED 3D INDOOR RECONSTRUCTION FROM POINT CLOUDS FOR “AS-BUILT” BIM

2015 ◽  
Vol II-3/W4 ◽  
pp. 17-24 ◽  
Author(s):  
S. Becker ◽  
M. Peter ◽  
D. Fritsch
Keyword(s):  
Point Clouds ◽  
Observation Data ◽  
Reconstruction Process ◽  
Building Model ◽  
3D Point Clouds ◽  
Building Models ◽  
Seamless Transition ◽  
Floor Plans ◽  
Knowledge Content ◽  
Realistic Geometries

The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data - here 3D point clouds - collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

scholarly journals SEMI-AUTOMATIC ROCK MASS GEOMETRY ANALYSIS FROM A DENSE 3D POINT CLOUD WITH DISCONTINUITYLAB

2020 ◽  
Vol V-2-2020 ◽  
pp. 679-686
Author(s):  
P. Caudal ◽  
E. Simonetto ◽  
V. Merrien-Soukatchoff ◽  
T. J. B. Dewez
Keyword(s):  
Rock Mass ◽  
Point Cloud ◽  
Point Clouds ◽  
Underground Mines ◽  
Open Pit ◽  
3D Point Cloud ◽  
Additional Information ◽  
3D Point Clouds ◽  
Geometry Analysis ◽  
2D And 3D

Abstract. 2D and 3D imageries can allow the optimization of rock mass exploitation (quarries, roads, rail networks, open pit, potentially tunnels and underground mines networks). The increasingly common use of photogrammetry makes it possible to obtain georeferenced 3D point clouds that are useful for understanding the rock mass. Indeed, new structural analysis solutions have been proposed since the advent of the 3D technologies. These methods are essentially focused on the production of digital stereonet. Production of additional information from 3D point clouds are possible to better define the structure of the rock mass, in particular the quantification of the discontinuities density. The aim of this paper is to test and validate a new method that provides statistics on the distances between the discontinuity planes. This solution is based on exploiting the information previously extracted from the segmentation of the discontinuity planes of a point cloud and their classification in family. In this article, the proposed solution is applied on two multiscale examples, firstly to validate it with a virtual synthetic outcrop and secondly to test it on a real outcrop. To facilitate these analyses, a software called DiscontinuityLab has been developed and used for the treatments.

Investigation on human environmental recognition from 2D and 3D point clouds by simulator and its comparison with self-localization by experimental robot

2017 ◽  
Vol 2017 (0) ◽  
pp. 2P2-D01
Author(s):  
Kota Takakura ◽  
Kiyoaki Takahashi ◽  
Tomokazu Takahashi ◽  
Masato Suzuki ◽  
Yasuhiko Arai ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Point Clouds ◽  
2D And 3D

scholarly journals The Fusion Strategy of 2D and 3D Information Based on Deep Learning: A Review

2021 ◽  
Vol 13 (20) ◽  
pp. 4029
Author(s):  
Jianghong Zhao ◽  
Yinrui Wang ◽  
Yuee Cao ◽  
Ming Guo ◽  
Xianfeng Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Information Integration ◽  
Point Clouds ◽  
Future Research ◽  
Accuracy Improvement ◽  
3D Point Clouds ◽  
3D Information ◽  
2D And 3D ◽  
Research Domain

Recently, researchers have realized a number of achievements involving deep-learning-based neural networks for the tasks of segmentation and detection based on 2D images, 3D point clouds, etc. Using 2D and 3D information fusion for the advantages of compensation and accuracy improvement has become a hot research topic. However, there are no critical reviews focusing on the fusion strategies of 2D and 3D information integration based on various data for segmentation and detection, which are the basic tasks of computer vision. To boost the development of this research domain, the existing representative fusion strategies are collected, introduced, categorized, and summarized in this paper. In addition, the general structures of different kinds of fusion strategies were firstly abstracted and categorized, which may inspire researchers. Moreover, according to the methods included in this paper, the 2D information and 3D information of different methods come from various kinds of data. Furthermore, suitable datasets are introduced and comparatively summarized to support the relative research. Last but not least, we put forward some open challenges and promising directions for future research.

scholarly journals Performance Evaluation of Robot Localization Using 2D and 3D Point Clouds

2017 ◽  
Vol 29 (5) ◽  
pp. 928-934
Author(s):  
Kiyoaki Takahashi ◽  
◽  
Takafumi Ono ◽  
Tomokazu Takahashi ◽  
Masato Suzuki ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Autonomous Mobile Robots ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Laser Range Finders ◽  
2D And 3D

Autonomous mobile robots need to acquire surrounding environmental information based on which they perform their self-localizations. Current autonomous mobile robots often use point cloud data acquired by laser range finders (LRFs) instead of image data. In the virtual robot autonomous traveling tests we have conducted in this study, we have evaluated the robot’s self-localization performance on Normal Distributions Transform (NDT) scan matching. This was achieved using 2D and 3D point cloud data to assess whether they perform better self-localizations in case of using 3D or 2D point cloud data.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Least Squares ◽  
Laser Scanner ◽  
Real Data ◽  
Point Clouds ◽  
Statistical Testing ◽  
Normal Vector ◽  
Engineering Structures ◽  
3D Point Clouds ◽  
Least Squares Adjustment ◽  
Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

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