scholarly journals VIDEOGRAMMETRY VS PHOTOGRAMMETRY FOR HERITAGE 3D RECONSTRUCTION

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 1157-1162 ◽  
Author(s):  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Ground Truth ◽  
Point Clouds ◽  
Fixed Time ◽  
Time Interval ◽  
Flat Surfaces ◽  
3D Data ◽  
Video Frames ◽  
Dense Image ◽  
Dense Point ◽  
Image Orientation

<p><strong>Abstract.</strong> In the last years we are witnessing an increasing quality (and quantity) of video streams and a growing capability of SLAM-based methods to derive 3D data from video. Video sequences can be easily acquired by non-expert surveyors and possibly used for 3D documentation purposes. The aim of the paper is to evaluate the possibility to perform 3D reconstructions of heritage scenarios using videos ("videogrammetry"), e.g. acquired with smartphones. Video frames are extracted from the sequence using a fixed-time interval and two advanced methods. Frames are then processed applying automated image orientation / Structure from Motion (SfM) and dense image matching / Multi-View Stereo (MVS) methods. Obtained 3D dense point clouds are the visually validated as well as compared with photogrammetric ground truth archived acquiring image with a reflex camera or analysing 3D data's noise on flat surfaces.</p>

scholarly journals Polylidar3D-Fast Polygon Extraction from 3D Data

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4819
Author(s):  
Jeremy Castagno ◽  
Ella Atkins
Keyword(s):  
Point Clouds ◽  
Autonomous Driving ◽  
Convex Polygons ◽  
Flat Surfaces ◽  
Plane Normal ◽  
3D Point Clouds ◽  
3D Data ◽  
Dense Point ◽  
Real World Datasets ◽  
Low Dimensional

Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data), organized point clouds (e.g., range images), or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D’s versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy.

scholarly journals Monitoring and Computation of the Volumes of Stockpiles of Bulk Material by Means of UAV Photogrammetric Surveying

2019 ◽  
Vol 11 (12) ◽  
pp. 1471 ◽  
Author(s):  
Grazia Tucci ◽  
Antonio Gebbia ◽  
Alessandro Conti ◽  
Lidia Fiorini ◽  
Claudio Lubello
Keyword(s):  
Bulk Material ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
3D Models ◽  
Satellite System ◽  
3D Data ◽  
Current Survey ◽  
Close Range ◽  
Global Navigation Satellite

The monitoring and metric assessment of piles of natural or man-made materials plays a fundamental role in the production and management processes of multiple activities. Over time, the monitoring techniques have undergone an evolution linked to the progress of measure and data processing techniques; starting from classic topography to global navigation satellite system (GNSS) technologies up to the current survey systems like laser scanner and close-range photogrammetry. Last-generation 3D data management software allow for the processing of increasingly truer high-resolution 3D models. This study shows the results of a test for the monitoring and computing of stockpile volumes of material coming from the differentiated waste collection inserted in the recycling chain, performed by means of an unmanned aerial vehicle (UAV) photogrammetric survey and the generation of 3D models starting from point clouds. The test was carried out with two UAV flight sessions, with vertical and oblique camera configurations, and using a terrestrial laser scanner for measuring the ground control points and as ground truth for testing the two survey configurations. The computations of the volumes were carried out using two software and comparisons were made both with reference to the different survey configurations and to the computation software.

scholarly journals A comparison of multi-view 3D reconstruction of a rock wall using several cameras and a laser scanner

2014 ◽  
Vol XL-5 ◽  
pp. 573-580 ◽  
Author(s):  
K. Thoeni ◽  
A. Giacomini ◽  
R. Murtagh ◽  
E. Kniest
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
Digital Cameras ◽  
Rock Wall ◽  
Geological Features ◽  
Dense Point ◽  
Sharp Edges

This work presents a comparative study between multi-view 3D reconstruction using various digital cameras and a terrestrial laser scanner (TLS). Five different digital cameras were used in order to estimate the limits related to the camera type and to establish the minimum camera requirements to obtain comparable results to the ones of the TLS. The cameras used for this study range from commercial grade to professional grade and included a GoPro Hero 1080 (5 Mp), iPhone 4S (8 Mp), Panasonic Lumix LX5 (9.5 Mp), Panasonic Lumix ZS20 (14.1 Mp) and Canon EOS 7D (18 Mp). The TLS used for this work was a FARO Focus 3D laser scanner with a range accuracy of ±2 mm. The study area is a small rock wall of about 6 m height and 20 m length. The wall is partly smooth with some evident geological features, such as non-persistent joints and sharp edges. Eight control points were placed on the wall and their coordinates were measured by using a total station. These coordinates were then used to georeference all models. A similar number of images was acquired from a distance of between approximately 5 to 10 m, depending on field of view of each camera. The commercial software package PhotoScan was used to process the images, georeference and scale the models, and to generate the dense point clouds. Finally, the open-source package CloudCompare was used to assess the accuracy of the multi-view results. Each point cloud obtained from a specific camera was compared to the point cloud obtained with the TLS. The latter is taken as ground truth. The result is a coloured point cloud for each camera showing the deviation in relation to the TLS data. The main goal of this study is to quantify the quality of the multi-view 3D reconstruction results obtained with various cameras as objectively as possible and to evaluate its applicability to geotechnical problems.

scholarly journals COMBINING AIRBORNE OBLIQUE CAMERA AND LIDAR SENSORS: INVESTIGATION AND NEW PERSPECTIVES

2018 ◽  
Vol XLII-1 ◽  
pp. 437-444 ◽  
Author(s):  
I. Toschi ◽  
F. Remondino ◽  
R. Rothe ◽  
K. Klimek
Keyword(s):  
Ground Truth ◽  
Point Clouds ◽  
Image Sensors ◽  
Lidar Data ◽  
Imaging Data ◽  
Ground Truth Data ◽  
Dense Image ◽  
Integrated Processing ◽  
Urban Mapping ◽  
Oblique Images

<p><strong>Abstract.</strong> Hybrid sensor solutions, that feature active laser and passive image sensors on the same platform, are rapidly entering the airborne market of topographic and urban mapping, offering new opportunities for an improved quality of geo-spatial products. In this perspective, a concurrent acquisition of LiDAR data and oblique imagery, seems to have all the potential to lead the airborne (urban) mapping sector a step forward. This contribution focuses on the first commercial example of such an integrated, all-in-one mapping solution, namely the Leica CityMapper hybrid sensor. By analysing two CityMapper datasets acquired over the city of Heilbronn (Germany) and Bordeaux (France), the paper investigates potential and challenges, w.r.t. (i) number and distribution of tie points between nadir and oblique images, (ii) strategy for image aerial triangulation (AT) and accuracy achievable w.r.t ground truth data, (iii) local noise level and completeness of dense image matching (DIM) point clouds w.r.t LiDAR data. Solutions for an integrated processing of the concurrently acquired ranging and imaging data are proposed, that open new opportunities for exploiting the real potential of both data sources.</p>

scholarly journals GENERATING ACCURATE 3D MODELS OF ARCHITECTURAL HERITAGE STRUCTURES USING LOW-COST CAMERA AND OPEN SOURCE ALGORITHMS

2017 ◽  
Vol XLII-5/W1 ◽  
pp. 99-104
Author(s):  
M. Zacharek ◽  
P. Delis ◽  
M. Kedzierski ◽  
A. Fryskowska
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Web Application ◽  
Low Cost ◽  
Digital Camera ◽  
Point Clouds ◽  
3D Models ◽  
Dense Image ◽  
Dense Point ◽  
Historical Heritage

These studies have been conductedusing non-metric digital camera and dense image matching algorithms, as non-contact methods of creating monuments documentation.In order toprocess the imagery, few open-source software and algorithms of generating adense point cloud from images have been executed. In the research, the OSM Bundler, VisualSFM software, and web application ARC3D were used. Images obtained for each of the investigated objects were processed using those applications, and then dense point clouds and textured 3D models were created. As a result of post-processing, obtained models were filtered and scaled.The research showedthat even using the open-source software it is possible toobtain accurate 3D models of structures (with an accuracy of a few centimeters), but for the purpose of documentation and conservation of cultural and historical heritage, such accuracy can be insufficient.

scholarly journals PLANAR CONSTRAINTS FOR AN IMPROVED UAV-IMAGE-BASED DENSE POINT CLOUD GENERATION

2015 ◽  
Vol XL-1/W4 ◽  
pp. 269-274 ◽  
Author(s):  
F. He ◽  
A. Habib ◽  
A. Al-Rawabdeh
Keyword(s):  
Point Cloud ◽  
Experimental Tests ◽  
Point Clouds ◽  
Geometric Information ◽  
Planar Surfaces ◽  
3D Point Clouds ◽  
Dense Image ◽  
Dense Point ◽  
Uav Image

In this paper, we proposed a new refinement procedure for the semi-global dense image matching. In order to remove outliers and improve the disparity image derived from the semi-global algorithm, both the local smoothness constraint and point cloud segments are utilized. Compared with current refinement technique, which usually assumes the correspondences between planar surfaces and 2D image segments, our proposed approach can effectively deal with object with both planar and curved surfaces. Meanwhile, since 3D point clouds contain more precise geometric information regarding to the reconstructed objects, the planar surfaces identified in our approach can be more accurate. In order to illustrate the feasibility of our approach, several experimental tests are conducted on both Middlebury test and real UAV-image datasets. The results demonstrate that our approach has a good performance on improving the quality of the derived dense image-based point cloud.

scholarly journals CLOUD-BASED SOLUTION FOR NATIONWIDE POWER LINE MAPPING

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 119-126 ◽  
Author(s):  
I. Toschi ◽  
D. Morabito ◽  
E. Grilli ◽  
F. Remondino ◽  
C. Carlevaro ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Point Clouds ◽  
Power Line ◽  
Data Generation ◽  
Reconstruction Algorithms ◽  
Web Based ◽  
Promising Alternative ◽  
3D Data ◽  
Dense Point ◽  
Point Cloud Classification

<p><strong>Abstract.</strong> Automatic tools for power line mapping and monitoring are increasingly required by modern societies. Since traditional methods, like ground-based onsite inspections, are very labour- and time-intensive, the use of Geomatics techniques is becoming the most promising solution. However, there is a need for an all-in-one solution that allows the entire 3D mapping pipeline in a nationwide data context. The aim of this paper is to introduce a novel cloud-based solution for nationwide power line mapping. The innovative aspects of the system are threefold. First, to exploit image-based 3D reconstruction algorithms to derive dense point clouds over power line corridors, thus demonstrating the potential of photogrammetry as a promising alternative to costly LiDAR surveys. Second, to supply an all-in-one web-based pipeline that automatically manages all steps of power line mapping, from 3D data generation to clearance anomaly detection. Finally, to exploit cloud-computing technology, to handle massive input data. First tests show promising results for (i) 3D image-based reconstruction, (ii) point cloud classification and (iii) anomaly detection.</p>

scholarly journals Stereo Model Selection and Point Cloud Filtering using an Out-of-Core Octree

2014 ◽  
Vol XL-3 ◽  
pp. 373-380 ◽  
Author(s):  
K. Wenzel ◽  
N. Haala ◽  
D. Fritsch
Keyword(s):  
Model Selection ◽  
Image Matching ◽  
Stereo Matching ◽  
Point Clouds ◽  
Outlier Rejection ◽  
Object Space ◽  
Massive Point ◽  
3D Data ◽  
Dense Image ◽  
Stereo Triangulation

Dense image matching methods enable the retrieval of dense surface information using any kind of imagery. The best quality can be achieved for highly overlapping datasets, which avoids occlusions and provides highly redundant observations. Thus, images are acquired close to each other. This leads to datasets with increasing size &ndash; especially when large scenes are captured. While image acquisition can be performed in relatively short time, more time is required for data processing due to the computational complexity of the involved algorithms. For the dense surface reconstruction task, <i>Multi-View Stereo</i> algorithms can be used – which are typically beneficial due to the efficiency of image matching on stereo models. Our dense image matching solution <i>SURE</i> uses such an approach, where the result of stereo matching is fused using a multi-stereo triangulation in order to exploit the available redundancy. One key challenge of such <i>Multi-View Stereo</i> methods is the selection of suitable stereo models, where object space information should be considered to avoid unnecessary processing. Subsequently, the dense image matching step provides up to one 3D point for each pixel, which leads to massive point clouds. This large amount of 3D data needs to be filtered and integrated efficiently in object space. Within this paper, we present an <i>out-of-core octree</i>, which enables neighborhood and overlap analysis between point clouds. It is used on low-resolution point clouds to support the stereo model selection. Also, this tree is designed for the processing of massive point clouds with low memory requirements and thus can be used to perform outlier rejection, redundancy removal and resampling.

scholarly journals Comparing Seven Methodogies for Rigid Alignment of Point Clouds with Focus on Frame-to-Frame Registration in Depth Sequences

2018 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Fernando Akio Yamada ◽  
Gilson Antonio Giraldi ◽  
Marcelo Bernardes Vieira ◽  
Liliane Rodrigues Almeida ◽  
Antonio Lopes Apolinário Jr.
Keyword(s):  
Missing Data ◽  
Mean Squared Error ◽  
Ground Truth ◽  
Point Clouds ◽  
Video Sequences ◽  
Depth Information ◽  
Rigid Registration ◽  
Temporal Sampling ◽  
3D Data ◽  
Depth Video

Pairwise rigid registration aims to find the rigid transformation that best registers two surfaces represented by point clouds. This work presents a comparison between seven algorithms, with different strategies to tackle rigid registration tasks. We focus on the frame-to-frame problem, in which the point clouds are extracted from a video sequence with depth information generating partial overlapping 3D data. We use both point clouds and RGB-D video streams in the experimental results. The former is considered under different viewpoints with the addition of a case-study simulating missing data. Since the ground truth rotation is provided, we discuss four different metrics to measure the rotation error in this case. Among the seven considered techniques, the Sparse ICP and Sparse ICP-CTSF outperform the other five ones in the point cloud registration experiments without considering incomplete data. However, the evaluation facing missing data indicates sensitivity for these methods against this problem and favors ICP-CTSF in such situations. In the tests with video sequences, the depth information is segmented in the first step, to get the target region. Next, the registration algorithms are applied and the average root mean squared error, rotation and translation errors are computed. Besides, we analyze the robustness of the algorithms against spatial and temporal sampling rates. We conclude from the experiments using a depth video sequences that ICP-CTSF is the best technique for frame-to-frame registration.

scholarly journals ASSEMBLING AN IMAGE AND POINT CLOUD DATASET FOR HERITAGE BUILDING SEMANTIC SEGMENTATION

2021 ◽  
Vol XLVI-M-1-2021 ◽  
pp. 539-546
Author(s):  
E. Pellis ◽  
A. Masiero ◽  
G. Tucci ◽  
M. Betti ◽  
P. Grussenmeyer
Keyword(s):  
Three Dimensional ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Limited Availability ◽  
Manual Intervention ◽  
3D Data ◽  
Class Definition ◽  
Heritage Buildings ◽  
Comparison Of The Results

Abstract. Creating three-dimensional as-built models from point clouds is still a challenging task in the Cultural Heritage environment. Nowadays, performing such task typically requires the quite time-consuming manual intervention of an expert operator, in particular to deal with the complexities and peculiarities of heritage buildings. Motivated by these considerations, the development of automatic or semi-automatic tools to ease the completion of such task has recently became a very hot topic in the research community. Among the tools that can be considered to such aim, the use of deep learning methods for the semantic segmentation and classification of 2D and 3D data seems to be one of the most promising approaches. Indeed, these kinds of methods have already been successfully applied in several applications enabling scene understanding and comprehension, and, in particular, to ease the process of geometrical and informative model creation. Nevertheless, their use in the specific case of heritage buildings is still quite limited, and the already published results not completely satisfactory. The quite limited availability of dedicated benchmarks for the considered task in the heritage context can also be one of the factors for the not so satisfying results in the literature.Hence, this paper aims at partially reducing the issues related to the limited availability of benchmarks in the heritage context by presenting a new dataset for semantic segmentation of heritage buildings. The dataset is composed by both images and point clouds of the considered buildings, in order to enable the implementation, validation and comparison of both point-based and multiview-based semantic segmentation approaches. Ground truth segmentation is provided, for both the images and point clouds related to each building, according to the class definition used in the ARCHdataset, hence potentially enabling also the integration and comparison of the results obtained on such dataset.

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