scholarly journals Grammar-based Automatic 3D Model Reconstruction from Terrestrial Laser Scanning Data

2014 ◽  
Vol XL-4 ◽  
pp. 335-340 ◽  
Author(s):  
Q. Yu ◽  
P. Helmholz ◽  
D. Belton ◽  
G. West
Keyword(s):  
Laser Scanning ◽  
Data File ◽  
Formal Grammar ◽  
Model Reconstruction ◽  
Reconstruction Process ◽  
Building Model ◽  
Building Models ◽  
3D Model Reconstruction ◽  
Data Interchange ◽  
3D Shapes

The automatic reconstruction of 3D buildings has been an important research topic during the last years. In this paper, a novel method is proposed to automatically reconstruct the 3D building models from segmented data based on pre-defined formal grammar and rules. Such segmented data can be extracted e.g. from terrestrial or mobile laser scanning devices. Two steps are considered in detail. The first step is to transform the segmented data into 3D shapes, for instance using the DXF (Drawing Exchange Format) format which is a CAD data file format used for data interchange between AutoCAD and other program. Second, we develop a formal grammar to describe the building model structure and integrate the pre-defined grammars into the reconstruction process. Depending on the different segmented data, the selected grammar and rules are applied to drive the reconstruction process in an automatic manner. Compared with other existing approaches, our proposed method allows the model reconstruction directly from 3D shapes and takes the whole building into account.

scholarly journals Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery

2019 ◽  
Vol 11 (14) ◽  
pp. 1660
Author(s):  
Partovi ◽  
Fraundorfer ◽  
Bahmanyar ◽  
Huang ◽  
Reinartz
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Missing Values ◽  
Model Reconstruction ◽  
Large Area ◽  
Building Model ◽  
Building Models ◽  
D Model ◽  
Very High

Recent advances in the availability of very high-resolution (VHR) satellite data together withefficient data acquisition and large area coverage have led to an upward trend in their applicationsfor automatic 3-D building model reconstruction which require large-scale and frequent updates,such as disaster monitoring and urban management. Digital Surface Models (DSMs) generatedfrom stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting inrough building shape representations. To handle 3-D building model reconstruction using suchlow-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs togetherwith orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satelliteimagery. The algorithm consists of multiple steps including building boundary extraction anddecomposition, image-based roof type classification, and initial roof parameter computation whichare prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM(nDSM) and to select the best one, a parameter optimization method based on exhaustive searchis used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building blockare intersected to reconstruct the 3-D model of connecting roofs. All corresponding experimentsare conducted on a dataset including four different areas of Munich city containing 208 buildingswith different degrees of complexity. The results are evaluated both qualitatively and quantitatively.According to the results, the proposed approach can reliably reconstruct 3-D building models, eventhe complex ones with several inner yards and multiple orientations. Furthermore, the proposedapproach provides a high level of automation by limiting the number of primitive roof types and byperforming automatic parameter initialization.

scholarly journals QUALITY ANALYSIS ON 3D BUIDLING MODELS RECONSTRUCTED FROM UAV IMAGERY

2016 ◽  
Vol XLI-B1 ◽  
pp. 1121-1126 ◽  
Author(s):  
M. Jarzabek-Rychard ◽  
M. Karpina
Keyword(s):  
Laser Scanning ◽  
Information Source ◽  
Optimal Solution ◽  
Ground Truth ◽  
Quality Analysis ◽  
Reconstruction Process ◽  
Building Models ◽  
3D Data ◽  
Recent Developments ◽  
Height Component

Recent developments in UAV technology and structure from motion techniques have effected that UAVs are becoming standard platforms for 3D data collection. Because of their flexibility and ability to reach inaccessible urban parts, drones appear as optimal solution for urban applications. Building reconstruction from the data collected with UAV has the important potential to reduce labour cost for fast update of already reconstructed 3D cities. However, especially for updating of existing scenes derived from different sensors (e.g. airborne laser scanning), a proper quality assessment is necessary. The objective of this paper is thus to evaluate the potential of UAV imagery as an information source for automatic 3D building modeling at LOD2. The investigation process is conducted threefold: (1) comparing generated SfM point cloud to ALS data; (2) computing internal consistency measures of the reconstruction process; (3) analysing the deviation of Check Points identified on building roofs and measured with a tacheometer. In order to gain deep insight in the modeling performance, various quality indicators are computed and analysed. The assessment performed according to the ground truth shows that the building models acquired with UAV-photogrammetry have the accuracy of less than 18 cm for the plannimetric position and about 15 cm for the height component.

Application of the Reverse Engineering to 3D Reconstruction on a Denture

2012 ◽  
Vol 490-495 ◽  
pp. 2032-2036
Author(s):  
Lei Zhang ◽  
Heng Wang ◽  
Hai Li Xu
Keyword(s):  
Reverse Engineering ◽  
Noise Suppression ◽  
Model Reconstruction ◽  
Contact Mode ◽  
Reconstruction Process ◽  
Cloud Data ◽  
Surface Patches ◽  
3D Model Reconstruction ◽  
Engineering Technique ◽  
Surface Data

In this paper, 3D model reconstruction on a denture that has undergone the run-in time is studied, using reverse engineering technique. In the stage of surface data acquisition, since the denture has very complicated and irregular contour, a non-contact mode is preferred. In point cloud data pre-processing, salient outliers are manually removed; Gaussian filter is used for noise suppression; curvature-based sampling is adopted for data reduction. In the model reconstruction stage, the surface reconstruction which is based on enclosed triangular Bezier surface patches is used for it can deal with arbitrary shape contours. Then, hole patching, smoothing and reduction are added for refinement of the surface creation. At last, the errors of the whole reconstruction process are measured and evaluated. The results indicate that our reverse engineering work has good performance, which can bring about comfortableness for the use of future dentures by patients.

3D Model Reconstruction Based on Laser Scanning Technique

2011 ◽  
Vol 48 (8) ◽  
pp. 081201
Author(s):  
Nguyen Tien Thanh Nguyen Tien Thanh ◽  
刘修国 Liu Xiuguo ◽  
王红平 Wang Hongping ◽  
于明旭 Yu Mingxu ◽  
周文浩 Zhou Wenhao
Keyword(s):  
Laser Scanning ◽  
3D Model ◽  
Model Reconstruction ◽  
Scanning Technique ◽  
3D Model Reconstruction

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 Evaluations of Three-Dimensional Building Model Reconstruction from LiDAR Point Clouds and Single-View Perspective Imagery

2014 ◽  
Vol XL-5 ◽  
pp. 597-600 ◽  
Author(s):  
F. Tsai ◽  
H. Chang
Keyword(s):  
Laser Scanning ◽  
Semantic Analysis ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Conjugate Points ◽  
Model Reconstruction ◽  
Perspective View ◽  
Single View ◽  
Building Model

This paper briefly presents two approaches for effective three-dimensional (3D) building model reconstruction from terrestrial laser scanning (TLS) data and single perspective view imagery and assesses their applicability to the reconstruction of 3D models of landmark or historical buildings. The collected LiDAR point clouds are registered based on conjugate points identified using a seven-parameter transformation system. Three dimensional models are generated using plan and surface fitting algorithms. The proposed single-view reconstruction (SVR) method is based on vanishing points and single-view metrology. More detailed models can also be generated according to semantic analysis of the façade images. Experimental results presented in this paper demonstrate that both TLS and SVR approaches can successfully produce accurate and detailed 3D building models from LiDAR point clouds or different types of single-view perspective images.

scholarly journals AN AUTOMATED 3D INDOOR TOPOLOGICAL NAVIGATION NETWORK MODELLING

2015 ◽  
Vol II-2/W2 ◽  
pp. 47-53 ◽  
Author(s):  
A. Jamali ◽  
A. A. Rahman ◽  
P. Boguslawski ◽  
C. M. Gold
Keyword(s):  
Indoor Environment ◽  
Laser Scanning ◽  
3D Models ◽  
Indoor Navigation ◽  
Control Points ◽  
Network Modelling ◽  
Building Model ◽  
Building Models ◽  
Data Acquisition Process ◽  
Topological Navigation

Indoor navigation is important for various applications such as disaster management and safety analysis. In the last decade, indoor environment has been a focus of wide research; that includes developing techniques for acquiring indoor data (e.g. Terrestrial laser scanning), 3D indoor modelling and 3D indoor navigation models. In this paper, an automated 3D topological indoor network generated from inaccurate 3D building models is proposed. In a normal scenario, 3D indoor navigation network derivation needs accurate 3D models with no errors (e.g. gap, intersect) and two cells (e.g. rooms, corridors) should touch each other to build their connections. The presented 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. For reducing time and cost of indoor building data acquisition process, Trimble LaserAce 1000 as surveying instrument is used. The modelling results were validated against an accurate geometry of indoor building environment which was acquired using Trimble M3 total station.

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