scholarly journals A VOXEL-BASED METADATA STRUCTURE FOR CHANGE DETECTION IN POINT CLOUDS OF LARGE-SCALE URBAN AREAS

2018 ◽  
Vol IV-2 ◽  
pp. 97-104
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Urban Environments ◽  
Detection Methods ◽  
Planar Surfaces ◽  
Occupancy Grids ◽  
Speed Up

Mobile laser scanning has not only the potential to create detailed representations of urban environments, but also to determine changes up to a very detailed level. An environment representation for change detection in large scale urban environments based on point clouds has drawbacks in terms of memory scalability. Volumes, however, are a promising building block for memory efficient change detection methods. The challenge of working with 3D occupancy grids is that the usual raycasting-based methods applied for their generation lead to artifacts caused by the traversal of unfavorable discretized space. These artifacts have the potential to distort the state of voxels in close proximity to planar structures. In this work we propose a raycasting approach that utilizes knowledge about planar surfaces to completely prevent this kind of artifacts. To demonstrate the capabilities of our approach, a method for the iterative volumetric approximation of point clouds that allows to speed up the raycasting by 36 percent is proposed.

scholarly journals Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Change Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Value ◽  
Detection Methods ◽  
Mountainous Catchments ◽  
Aerial Vehicle ◽  
Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

scholarly journals CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS

2020 ◽  
Vol V-2-2020 ◽  
pp. 703-710
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Lidar Data ◽  
Efficient Manner ◽  
Incomplete Observations ◽  
Novel Method

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

scholarly journals A FRAMEWORK FOR VOXEL-BASED GLOBAL SCALE MODELING OF URBAN ENVIRONMENTS

2016 ◽  
Vol XLII-2/W1 ◽  
pp. 45-51 ◽  
Author(s):  
Joachim Gehrung ◽  
Marcus Hebel ◽  
Michael Arens ◽  
Uwe Stilla
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Global Scale ◽  
Urban Environments ◽  
Scale Modeling ◽  
Volumetric Models ◽  
University Of Munich ◽  
Urban Models ◽  
The Right

The generation of 3D city models is a very active field of research. Modeling environments as point clouds may be fast, but has disadvantages. These are easily solvable by using volumetric representations, especially when considering selective data acquisition, change detection and fast changing environments. Therefore, this paper proposes a framework for the volumetric modeling and visualization of large scale urban environments. Beside an architecture and the right mix of algorithms for the task, two compression strategies for volumetric models as well as a data quality based approach for the import of range measurements are proposed. The capabilities of the framework are shown on a mobile laser scanning dataset of the Technical University of Munich. Furthermore the loss of the compression techniques is evaluated and their memory consumption is compared to that of raw point clouds. The presented results show that generation, storage and real-time rendering of even large urban models are feasible, even with off-the-shelf hardware.

Dynamic multi-sensor platform for efficient 3D-digitalization of cities: part II

2021 ◽  
pp. 1-9
Author(s):  
Angel-Ivan Garcia-Moreno
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Point Clouds ◽  
Urban Environments ◽  
Data Set ◽  
Urban Scenes ◽  
3D Data ◽  
Sensor Platform ◽  
Laser Scanners ◽  
Potential Applications

Abstract The digitization of geographic environments, such as cities and archaeological sites, is of priority interest to the scientific community due to its potential applications. But there are still several issues to address. There are various digitization strategies, which include terrestrial/ airborne platforms and composed of various sensors, among the most common, cameras and laser scanners. A comprehensive methodology is presented to reconstruct urban environments using a mobile land platform. All the implemented stages are described, which includes the acquisition, processing, and correlation of the data delivered by a Velodyne HDL-64E scanner, a spherical camera, GPS, and inertial systems. The process to merge several point clouds to build a large-scale map is described, as well as the generation of surfaces. Being able to render large urban areas using a low density of points but without losing the details of the structures within the urban scenes. The proposal is evaluated using several metrics, for example, Coverage and Root-Mean-Square-Error (RSME). The results are compared against 3 methodologies reported in the literature. Obtaining better results in the 2D/3D data fusion process and the generation of surfaces. The described method has a low RMSE (0.79) compared to the other methods and a runtime of approximately 40 seconds to process each data set (point cloud, panoramic image, and inertial data). In general, the proposed methodology shows a more homogeneous density distribution without losing the details, that is, it conserves the spatial distribution of the points, but with fewer data.

scholarly journals INVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 397-404
Author(s):  
A. Nurunnabi ◽  
F. N. Teferle ◽  
J. Li ◽  
R. C. Lindenbergh ◽  
S. Parvaz
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Urban Environments ◽  
Batch Size ◽  
Block Partition ◽  
Significant Difference ◽  
Irregular Data ◽  
3D Scene

Abstract. Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL algorithms degrades, because point clouds are often sparse and have irregular data format. As a result, point clouds are regularly first transformed into voxel grids or image collections. PointNet was the first promising algorithm that feeds point clouds directly into the DL architecture. Although PointNet achieved remarkable performance on indoor point clouds, its performance has not been extensively studied in large-scale outdoor point clouds. So far, we know, no study on large-scale aerial point clouds investigates the sensitivity of the hyper-parameters used in the PointNet. This paper evaluates PointNet’s performance for semantic segmentation through three large-scale Airborne Laser Scanning (ALS) point clouds of urban environments. Reported results show that PointNet has potential in large-scale outdoor scene semantic segmentation. A remarkable limitation of PointNet is that it does not consider local structure induced by the metric space made by its local neighbors. Experiments exhibit PointNet is expressively sensitive to the hyper-parameters like batch-size, block partition and the number of points in a block. For an ALS dataset, we get significant difference between overall accuracies of 67.5% and 72.8%, for the block sizes of 5m × 5m and 10m × 10m, respectively. Results also discover that the performance of PointNet depends on the selection of input vectors.

scholarly journals SEMANTIC LABELLING OF ROAD FURNITURE IN MOBILE LASER SCANNING DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 247-254 ◽  
Author(s):  
F. Li ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
3D Point Clouds ◽  
Promising Solution ◽  
Novel Method ◽  
Detailed Interpretation ◽  
Semantic Labelling

Road furniture semantic labelling is vital for large scale mapping and autonomous driving systems. Much research has been investigated on road furniture interpretation in both 2D images and 3D point clouds. Precise interpretation of road furniture in mobile laser scanning data still remains unexplored. In this paper, a novel method is proposed to interpret road furniture based on their logical relations and functionalities. Our work represents the most detailed interpretation of road furniture in mobile laser scanning data. 93.3 % of poles are correctly extracted and all of them are correctly recognised. 94.3 % of street light heads are detected and 76.9 % of them are correctly identified. Despite errors arising from the recognition of other components, our framework provides a promising solution to automatically map road furniture at a detailed level in urban environments.

scholarly journals AUTOMATED COARSE REGISTRATION OF POINT CLOUDS IN 3D URBAN SCENES USING VOXEL BASED PLANE CONSTRAINT

2017 ◽  
Vol IV-2/W4 ◽  
pp. 185-191 ◽  
Author(s):  
Y. Xu ◽  
R. Boerner ◽  
W. Yao ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Coordinate Frame ◽  
Small Individual ◽  
Planar Surfaces ◽  
Coarse Registration ◽  
Transformation Parameters ◽  
Normal Vectors

For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

2021 ◽  
Author(s):  
Matthew B. Creasy ◽  
Wade Travis Tinkham ◽  
Chad M. Hoffman ◽  
Jody C. Vogeler
Keyword(s):  
Forest Structure ◽  
Ponderosa Pine ◽  
Laser Scanning ◽  
Point Clouds ◽  
System Structure ◽  
Detection Methods ◽  
Unmanned Aerial System ◽  
Individual Tree ◽  
Detection Rates ◽  
Ponderosa Pine Forests

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.

scholarly journals Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

2019 ◽  
Vol 11 (12) ◽  
pp. 1453 ◽  
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Lili Lin ◽  
Chenglu Wen ◽  
Chenhui Yang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Laser Scanning ◽  
Large Scale ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Point Clouds ◽  
Traffic Sign ◽  
Traffic Signs ◽  
3D Point Clouds ◽  
Traffic Environment

Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

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