Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAractérisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6–60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice.

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DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b5-685-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 685-692 ◽

Cited By ~ 1

Author(s):

Robert Niederheiser ◽

Martin Mokroš ◽

Julia Lange ◽

Helene Petschko ◽

Günther Prasicek ◽

...

Keyword(s):

Point Cloud ◽

State Of The Art ◽

User Interaction ◽

Point Clouds ◽

Rock Face ◽

3D Point Clouds ◽

Software Packages ◽

User Friendly ◽

Insight Into

Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives. <br><br> We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (1.2.0.286), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud. <br><br> While PhotoScan and Pix4D offer the user-friendliest workflows, they are also “black-box” programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.

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DETECTING PROBABLE ROCKFALLS IN OPEN PIT MINES BASED ON UAV POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-1091-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 1091-1096

Author(s):

S. M. Yousefi ◽

H. Arefi ◽

A. Bahroudi

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Open Pit ◽

Rock Falls ◽

Open Pit Mines ◽

Cloud Data ◽

Rock Face ◽

Geological Features ◽

Active Research

Abstract. Stability analysis and studying the geological features of rocks and mines have been active research topic for many years. Consequently, it is very important being prepared for probable hazards and having the ability to rescue from earth disasters, in particular in rocks and open pit mines. For this purpose, several methods have been used to measure fractures of a rock face. Among these methods are manual techniques, photogrammetric measurements, and laser scanning based techniques. With the proliferation of unmanned aerial vehicles (UAVs), these systems have been widely used in geological projects recently. Especially in the situation that the case study is very hard to be reached. In this paper, a method is developed to detect the most probable rock fall. After doing some pre-processing, RANSAC algorithm is used to fit planes to the point cloud. Then, intersections of these planes with the point cloud are computed. After some refinements on these intersections, the probable rockfalls are obtained. Point cloud analysis have some advantages over conventional image-based methods; especially in case of probable rock falls, which might be hard to detect using the rock images. However, analyzing point cloud data usually is complicated and computationally expensive.

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DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b5-685-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 685-692 ◽

Cited By ~ 15

Author(s):

Robert Niederheiser ◽

Martin Mokroš ◽

Julia Lange ◽

Helene Petschko ◽

Günther Prasicek ◽

...

Keyword(s):

Point Cloud ◽

State Of The Art ◽

User Interaction ◽

Point Clouds ◽

Rock Face ◽

3D Point Clouds ◽

Software Packages ◽

User Friendly ◽

Insight Into

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Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

Remote Sensing ◽

10.3390/rs13050957 ◽

2021 ◽

Vol 13 (5) ◽

pp. 957

Author(s):

Guglielmo Grechi ◽

Matteo Fiorucci ◽

Gian Marco Marmoni ◽

Salvatore Martino

Keyword(s):

Surface Temperature ◽

Infrared Thermography ◽

Point Cloud ◽

Engineering Geology ◽

Point Clouds ◽

Jointed Rock ◽

Geometric Accuracy ◽

Rock Masses ◽

Natural Systems ◽

Temperature Distributions

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

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Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

Remote Sensing ◽

10.3390/rs13112195 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2195

Author(s):

Shiming Li ◽

Xuming Ge ◽

Shengfu Li ◽

Bo Xu ◽

Zhendong Wang

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Registration Method ◽

Cloud Data ◽

Linear Feature ◽

Data Registration ◽

Source Point ◽

Urban Remote Sensing ◽

Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

Sensors ◽

10.3390/s21041304 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1304

Author(s):

Wenchao Wu ◽

Yongguang Hu ◽

Yongzong Lu

Keyword(s):

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Parametric Surface ◽

B Spline ◽

Surface Modelling ◽

Modelling Method ◽

3D Architecture ◽

Tea Leaf ◽

Fitting Error

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

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Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

Remote Sensing ◽

10.3390/rs13142770 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2770

Author(s):

Shengjing Tian ◽

Xiuping Liu ◽

Meng Liu ◽

Yuhao Bian ◽

Junbin Gao ◽

...

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Vehicle Tracking ◽

Complex Nature ◽

Global Feature ◽

Tracking Accuracy ◽

State Estimator ◽

Latent Space ◽

3D Shapes ◽

Cloud Tracking

Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.

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

Sensors ◽

10.3390/s21041228 ◽

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.

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Matching Point Clouds with STL Models by Using the Principle Component Analysis and a Decomposition into Geometric Primitives

Applied Sciences ◽

10.3390/app11052268 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2268

Author(s):

Erika Straková ◽

Dalibor Lukáš ◽

Zdenko Bobovský ◽

Tomáš Kot ◽

Milan Mihola ◽

...

Keyword(s):

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Component Analysis ◽

Language Models ◽

Computational Time ◽

Dominant Eigenvalue ◽

Eigenvalues And Eigenvectors ◽

Correct Model ◽

Geometric Primitives

While repairing industrial machines or vehicles, recognition of components is a critical and time-consuming task for a human. In this paper, we propose to automatize this task. We start with a Principal Component Analysis (PCA), which fits the scanned point cloud with an ellipsoid by computing the eigenvalues and eigenvectors of a 3-by-3 covariant matrix. In case there is a dominant eigenvalue, the point cloud is decomposed into two clusters to which the PCA is applied recursively. In case the matching is not unique, we continue to distinguish among several candidates. We decompose the point cloud into planar and cylindrical primitives and assign mutual features such as distance or angle to them. Finally, we refine the matching by comparing the matrices of mutual features of the primitives. This is a more computationally demanding but very robust method. We demonstrate the efficiency and robustness of the proposed methodology on a collection of 29 real scans and a database of 389 STL (Standard Triangle Language) models. As many as 27 scans are uniquely matched to their counterparts from the database, while in the remaining two cases, there is only one additional candidate besides the correct model. The overall computational time is about 10 min in MATLAB.

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