scholarly journals Extended Hybrid Region Growing Segmentation of Point Clouds with Different Resolution from Dense Aerial Image Matching

2013 ◽  
Author(s):  
Mohammad Omidalizarandi ◽  
Mohammad Saadatseresht
Keyword(s):  
Image Matching ◽  
Region Growing ◽  
Point Clouds ◽  
Aerial Image ◽  
Hybrid Region ◽  
Region Growing Segmentation

scholarly journals Segmentation and Classification of Point Clouds from Dense Aerial Image Matching

2013 ◽  
Vol 5 (4) ◽  
pp. 33-51 ◽  
Author(s):  
Mohammad Omidalizarandi ◽  
Mohammad Saadatseresht
Keyword(s):  
Image Matching ◽  
Point Clouds ◽  
Aerial Image

scholarly journals Historic Timber Roof Structure Reconstruction through Automated Analysis of Point Clouds

2022 ◽  
Vol 8 (1) ◽  
pp. 10
Author(s):  
Taşkın Özkan ◽  
Norbert Pfeifer ◽  
Gudrun Styhler-Aydın ◽  
Georg Hochreiner ◽  
Ulrike Herbig ◽  
...  
Keyword(s):  
Laser Scanning ◽  
3D Model ◽  
Region Growing ◽  
Point Clouds ◽  
Automated Analysis ◽  
Structural Elements ◽  
Roof Structure ◽  
Main Target ◽  
Side Face ◽  
Region Growing Segmentation

We present a set of methods to improve the automation of the parametric 3D modeling of historic roof structures using terrestrial laser scanning (TLS) point clouds. The final product of the TLS point clouds consist of 3D representation of all objects, which were visible during the scanning, including structural elements, wooden walking ways and rails, roof cover and the ground; thus, a new method was applied to detect and exclude the roof cover points. On the interior roof points, a region-growing segmentation-based beam side face searching approach was extended with an additional method that splits complex segments into linear sub-segments. The presented workflow was conducted on an entire historic roof structure. The main target is to increase the automation of the modeling in the context of completeness. The number of manually counted beams served as reference to define a completeness ratio for results of automatically modeling beams. The analysis shows that this approach could increase the quantitative completeness of the full automatically generated 3D model of the roof structure from 29% to 63%.

scholarly journals AUOTOMATIC CLASSIFICATION OF POINT CLOUDS EXTRACTED FROM ULTRACAM STEREO IMAGES

2015 ◽  
Vol XL-1-W5 ◽  
pp. 493-496 ◽  
Author(s):  
M. Modiri ◽  
M. Masumi ◽  
A. Eftekhari
Keyword(s):  
Image Matching ◽  
Vegetation Index ◽  
Region Growing ◽  
Point Clouds ◽  
Maximum Height ◽  
Color Characteristics ◽  
Matching Technique ◽  
Dense Point ◽  
Elevation Model

Automatic extraction of building roofs, street and vegetation are a prerequisite for many GIS (Geographic Information System) applications, such as urban planning and 3D building reconstruction. Nowadays with advances in image processing and image matching technique by using feature base and template base image matching technique together dense point clouds are available. Point clouds classification is an important step in automatic features extraction. Therefore, in this study, the classification of point clouds based on features color and shape are implemented. <br><br> We use two images by proper overlap getting by Ultracam-x camera in this study. The images are from Yasouj in IRAN. It is semi-urban area by building with different height. Our goal is classification buildings and vegetation in these points. <br><br> In this article, an algorithm is developed based on the color characteristics of the point’s cloud, using an appropriate DEM (Digital Elevation Model) and points clustering method. So that, firstly, trees and high vegetation are classified by using the point’s color characteristics and vegetation index. Then, bare earth DEM is used to separate ground and non-ground points. <br><br> Non-ground points are then divided into clusters based on height and local neighborhood. One or more clusters are initialized based on the maximum height of the points and then each cluster is extended by applying height and neighborhood constraints. Finally, planar roof segments are extracted from each cluster of points following a region-growing technique.

scholarly journals HIGH DENSITY AERIAL IMAGE MATCHING: STATE-OF-THE-ART AND FUTURE PROSPECTS

2016 ◽  
Vol XLI-B4 ◽  
pp. 625-630 ◽  
Author(s):  
N. Haala ◽  
S. Cavegn
Keyword(s):  
Image Matching ◽  
State Of The Art ◽  
Point Clouds ◽  
High Density ◽  
Data Capture ◽  
Aerial Image ◽  
Special Focus ◽  
Second Phase ◽  
High Quality ◽  
Geometric Data

Ongoing innovations in matching algorithms are continuously improving the quality of geometric surface representations generated automatically from aerial images. This development motivated the launch of the joint ISPRS/EuroSDR project “Benchmark on High Density Aerial Image Matching”, which aims on the evaluation of photogrammetric 3D data capture in view of the current developments in dense multi-view stereo-image matching. Originally, the test aimed on image based DSM computation from conventional aerial image flights for different landuse and image block configurations. The second phase then put an additional focus on high quality, high resolution 3D geometric data capture in complex urban areas. This includes both the extension of the test scenario to oblique aerial image flights as well as the generation of filtered point clouds as additional output of the respective multi-view reconstruction. The paper uses the preliminary outcomes of the benchmark to demonstrate the state-of-the-art in airborne image matching with a special focus of high quality geometric data capture in urban scenarios.

scholarly journals UNSUPERVISED SEGMENTATION OF INDOOR 3D POINT CLOUD: APPLICATION TO OBJECT-BASED CLASSIFICATION

2020 ◽  
Vol XLIV-4/W1-2020 ◽  
pp. 111-118 ◽  
Author(s):  
F. Poux ◽  
C. Mattes ◽  
L. Kobbelt
Keyword(s):  
Point Cloud ◽  
Computing Time ◽  
Region Growing ◽  
Point Clouds ◽  
Unsupervised Segmentation ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Object Based ◽  
Indoor Scenes ◽  
Region Growing Segmentation

Abstract. Point cloud data of indoor scenes is primarily composed of planar-dominant elements. Automatic shape segmentation is thus valuable to avoid labour intensive labelling. This paper provides a fully unsupervised region growing segmentation approach for efficient clustering of massive 3D point clouds. Our contribution targets a low-level grouping beneficial to object-based classification. We argue that the use of relevant segments for object-based classification has the potential to perform better in terms of recognition accuracy, computing time and lowers the manual labelling time needed. However, fully unsupervised approaches are rare due to a lack of proper generalisation of user-defined parameters. We propose a self-learning heuristic process to define optimal parameters, and we validate our method on a large and richly annotated dataset (S3DIS) yielding 88.1% average F1-score for object-based classification. It permits to automatically segment indoor point clouds with no prior knowledge at commercially viable performance and is the foundation for efficient indoor 3D modelling in cluttered point clouds.

Supervoxel-Based Region Growing Segmentation for Point Cloud Data

2020 ◽  
pp. 2154007
Author(s):  
Nan Luo ◽  
Yuanyuan Jiang ◽  
Quan Wang
Keyword(s):  
Point Cloud ◽  
High Efficiency ◽  
Region Growing ◽  
Experimental Tests ◽  
Point Clouds ◽  
Spatial Distance ◽  
Local Geometry ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Region Growing Segmentation

Point cloud segmentation is a crucial fundamental step in 3D reconstruction, object recognition and scene understanding. This paper proposes a supervoxel-based point cloud segmentation algorithm in region growing principle to solve the issues of inaccurate boundaries and nonsmooth segments in the existing methods. To begin with, the input point cloud is voxelized and then pre-segmented into sparse supervoxels by flow constrained clustering, considering the spatial distance and local geometry between voxels. Afterwards, plane fitting is applied to the over-segmented supervoxels and seeds for region growing are selected with respect to the fitting residuals. Starting from pruned seed patches, adjacent supervoxels are merged in region growing style to form the final segments, according to the normalized similarity measure that integrates the smoothness and shape constraints of supervoxels. We determine the values of parameters via experimental tests, and the final results show that, by voxelizing and pre-segmenting the point clouds, the proposed algorithm is robust to noises and can obtain smooth segmentation regions with accurate boundaries in high efficiency.

scholarly journals Investigation on Roof Segmentation for 3D Building Reconstruction from Aerial LIDAR Point Clouds

2019 ◽  
Vol 9 (21) ◽  
pp. 4674 ◽  
Author(s):  
Raffaele Albano
Keyword(s):  
Region Growing ◽  
Point Clouds ◽  
Operating Conditions ◽  
Information Modeling ◽  
World Heritage Site ◽  
Computational Time ◽  
Building Modeling ◽  
Segmentation Approach ◽  
Region Growing Segmentation ◽  
Building Reconstruction

Three-dimensional (3D) reconstruction techniques are increasingly used to obtain 3D representations of buildings due to the broad range of applications for 3D city models related to sustainability, efficiency and resilience (i.e., energy demand estimation, estimation of the propagation of noise in an urban environment, routing and accessibility, flood or seismic damage assessment). With advancements in airborne laser scanning (ALS), 3D modeling of urban topography has increased its potential to automatize extraction of the characteristics of individual buildings. In 3D building modeling from light detection and ranging (LIDAR) point clouds, one major challenging issue is how to efficiently and accurately segment building regions and extract rooftop features. This study aims to present an investigation and critical comparison of two different fully automatic roof segmentation approaches for 3D building reconstruction. In particular, the paper presents and compares a cluster-based roof segmentation approach that uses (a) a fuzzy c-means clustering method refined through a density clustering and connectivity analysis, and (b) a region growing segmentation approach combined with random sample consensus (RANSAC) method. In addition, a robust 2.5D dual contouring method is utilized to deliver watertight 3D building modeling from the results of each proposed segmentation approach. The benchmark LIDAR point clouds and related reference data (generated by stereo plotting) of 58 buildings over downtown Toronto (Canada), made available to the scientific community by the International Society for Photogrammetry and Remote Sensing (ISPRS), have been used to evaluate the quality of the two proposed segmentation approaches by analysing the geometrical accuracy of the roof polygons. Moreover, the results of both approaches have been evaluated under different operating conditions against the real measurements (based on archive documentation and celerimetric surveys realized by a total station system) of a complex building located in the historical center of Matera (UNESCO world heritage site in southern Italy) that has been manually reconstructed in 3D via traditional Building Information Modeling (BIM) technique. The results demonstrate that both methods reach good performance metrics in terms of geometry accuracy. However, approach (b), based on region growing segmentation, exhibited slightly better performance but required greater computational time than the clustering-based approach.

scholarly journals HIGH DENSITY AERIAL IMAGE MATCHING: STATE-OF-THE-ART AND FUTURE PROSPECTS

2016 ◽  
Vol XLI-B4 ◽  
pp. 625-630 ◽  
Author(s):  
N. Haala ◽  
S. Cavegn
Keyword(s):  
Image Matching ◽  
State Of The Art ◽  
Point Clouds ◽  
High Density ◽  
Data Capture ◽  
Aerial Image ◽  
Special Focus ◽  
Second Phase ◽  
High Quality ◽  
Geometric Data

Ongoing innovations in matching algorithms are continuously improving the quality of geometric surface representations generated automatically from aerial images. This development motivated the launch of the joint ISPRS/EuroSDR project “Benchmark on High Density Aerial Image Matching”, which aims on the evaluation of photogrammetric 3D data capture in view of the current developments in dense multi-view stereo-image matching. Originally, the test aimed on image based DSM computation from conventional aerial image flights for different landuse and image block configurations. The second phase then put an additional focus on high quality, high resolution 3D geometric data capture in complex urban areas. This includes both the extension of the test scenario to oblique aerial image flights as well as the generation of filtered point clouds as additional output of the respective multi-view reconstruction. The paper uses the preliminary outcomes of the benchmark to demonstrate the state-of-the-art in airborne image matching with a special focus of high quality geometric data capture in urban scenarios.

scholarly journals Benchmarking High Density Image Matching for Oblique Airborne Imagery

2014 ◽  
Vol XL-3 ◽  
pp. 45-52 ◽  
Author(s):  
S. Cavegn ◽  
N. Haala ◽  
S. Nebiker ◽  
M. Rothermel ◽  
P. Tutzauer
Keyword(s):  
Image Matching ◽  
Laser Scanning ◽  
Point Clouds ◽  
High Density ◽  
Data Capture ◽  
Aerial Image ◽  
Test Bed ◽  
Test Scenario ◽  
3D Point Clouds ◽  
Density Image

Both, improvements in camera technology and new pixel-wise matching approaches triggered the further development of software tools for image based 3D reconstruction. Meanwhile research groups as well as commercial vendors provide photogrammetric software to generate dense, reliable and accurate 3D point clouds and Digital Surface Models (DSM) from highly overlapping aerial images. In order to evaluate the potential of these algorithms in view of the ongoing software developments, a suitable test bed is provided by the ISPRS/EuroSDR initiative <i>Benchmark on High Density Image Matching for DSM Computation</i>. This paper discusses the proposed test scenario to investigate the potential of dense matching approaches for 3D data capture from oblique airborne imagery. For this purpose, an oblique aerial image block captured at a GSD of 6 cm in the west of Zürich by a Leica RCD30 Oblique Penta camera is used. Within this paper, the potential test scenario is demonstrated using matching results from two software packages, Agisoft PhotoScan and SURE from University of Stuttgart. As oblique images are frequently used for data capture at building facades, 3D point clouds are mainly investigated at such areas. Reference data from terrestrial laser scanning is used to evaluate data quality from dense image matching for several facade patches with respect to accuracy, density and reliability.

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