bluuebunny/crossref_metadata_2025 huggingface dataset:
UNSUPERVISED MULTI-CONSTRAINT DEEP NEURAL NETWORK FOR DENSE IMAGE MATCHING
Authors: Yuan W. , Fan Z. , Yuan X. , Gong J. , Shibasaki R.
Published: August 2020

Abstract. Dense image matching is essential to photogrammetry applications, including Digital Surface Model (DSM) generation, three dimensional (3D) reconstruction, and object detection and recognition. The development of an efficient and robust method for dense image matching has been one of the technical challenges due to high variations in illumination and ground features of aerial images of large areas. Nowadays, due to the development of deep learning technology, deep neural network-based algorithms outperform traditional methods on a variety of tasks such as object detection, semantic segmentation and stereo matching. The proposed network includes cost-volume computation, cost-volume aggregation, and disparity prediction. It starts with a pre-trained VGG-16 network as a backend and using the U-net architecture with nine layers for feature map extraction and a correlation layer for cost volume calculation, after that a guided filter based cost aggregation is adopted for cost volume filtering and finally the soft Argmax function is utilized for disparity prediction. The experimental conducted on a UAV dataset demonstrated that the proposed method achieved the RMSE (root mean square error) of the reprojection error better than 1 pixel in image coordinate and in-ground positioning accuracy within 2.5 ground sample distance. The comparison experiments on KITTI 2015 dataset shows the proposed unsupervised method even comparably with other supervised methods.
COMPARATIVE EVALUATION OF DERIVED IMAGE AND LIDAR POINT CLOUDS FROM UAV-BASED MOBILE MAPPING SYSTEMS
Authors: Zhou T. , Hasheminasab S. M. , Lin Y.-C. , Habib A.
Published: August 2020

Abstract. Unmanned aerial vehicles (UAVs) have been widely used for 3D reconstruction/modelling in various applications such as precision agriculture, coastal monitoring, and emergency management. For such mapping applications, camera and LiDAR are the two most commonly used sensors. Mapping with imagery-based approaches is considered to be an economical and effective option and is often conducted using Structure from Motion (SfM) techniques where point clouds and orthophotos are generated. In addition to UAV photogrammetry, point clouds of the area of interest can also be directly derived from LiDAR sensors onboard UAVs equipped with global navigation satellite systems/inertial navigation systems (GNSS/INS). In this study, a custom-built UAV-based mobile mapping system is used to simultaneously collect imagery and LiDAR data. Derived LiDAR and image-based point clouds are investigated and compared in terms of their absolute and relative accuracy. Furthermore, stability of the system calibration parameters for the camera and LiDAR sensors are studied using temporal datasets. The results show that while LiDAR point clouds demonstrate a high absolute accuracy over time, image-based point clouds are not as accurate as LiDAR due to instability of the camera interior orientation parameters.
PRELIMINARY INVESTIGATION INTO THE GEOMETRIC CALIBRATION OF THE MICASENSE REDEDGE-M MULTISPECTRAL CAMERA
Authors: Frontera F. , Smith M. J. , Marsh S.
Published: August 2020

Abstract. Multispectral cameras, in the past the prerogative of Remote Sensing (RS) applications via satellites and manned aircraft, are becoming increasingly used in photogrammetric applications. Moreover, the ubiquitous use of Unmanned Aerial Vehicles (UAVs) has created a need for the miniaturisation of sensors, which has contributed to the availability of a wide range of relatively low-cost and lightweight cameras. Therefore, small multispectral cameras mounted on UAVs provide an effective and low-cost solution when it comes to acquiring airborne radiometric data.With the growing interest for such sensors to perform photogrammetric tasks, camera calibration remains an essential step in order to obtain reliable and geometrically accurate information.This paper will investigate the camera calibration parameters between the five bands of the MicaSense RedEdge-M sensor from laboratory trials. The results of the camera calibration will be obtained from the use, primarily, of Australis software and a calibration frame within the Nottingham Geospatial Institute. The variations of the parameters demonstrate the need for distortion correction separately within each band before using the images for photogrammetry.
INDOOR SCENE REGISTRATION BASED ON KEY POINTS SAMPLING AND HIERARCHICAL FEATURE LEARNING
Authors: Ai M. , Liu C. , Shen H. , Cheng F.
Published: August 2020

Abstract. PointNet has been widely considered as a popular representation for unstructured point clouds with the aim of classification and segmentation. To date, recent researches represent the limitation of the PointNet to pose estimation and alignment of real environment, due to the low performance in pattern learning to complex scenes. This paper presents an end-to-end deep learning method for point clouds registration of indoor environment. The proposed method involves three steps. Firstly, feature pre-processing extracts the key-points by adaptive Harris 3D algorithm and generate the local group by point grouping. Second, hierarchical feature learning network is trained to describe the local group as feature descriptors. Finally, loss function between feature descriptor is trained. The key contribution is that we innovatively use the key-points to generate multi-layer feature vector, which can provide the contextual local features of the indoor environment. The results shows that our method achieves comparable registration accuracy to the present state-of-art geometric methods in the indoor environment. We comprehensively validate the accuracy of our approach using S3DIS dataset. The high accuracy demonstrates that our method can be used in point clouds registration accurately.
AN ADAPTED CONNECTED COMPONENT LABELING FOR CLUSTERING NON-PLANAR OBJECTS FROM AIRBORNE LIDAR POINT CLOUD
Authors: Aissou B. , Belhadj Aissa A.
Published: August 2020

Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.
WEIGHTED ICP POINT CLOUDS REGISTRATION BY SEGMENTATION BASED ON EIGENFEATURES CLUSTERING
Authors: Franzini M. , Manzino A. M. , Casella V.
Published: August 2020

Abstract. Dense point clouds can be nowadays considered the main product of UAV (Unmanned Aerial Vehicle) photogrammetric processing and clouds registration is still a key aspect in case of blocks acquired apart. In the paper some overlapping datasets, acquired with a multispectral Parrot Sequoia camera above some rice fields, are analysed in a single block approach. Since the sensors is equipped with a navigation-grade sensor, the georeferencing information is affected by large errors and the so obtained dense point clouds are significantly far apart: to register them the Iterative Closes Point (ICP) technique is applied. ICP convergence is fundamentally based on the correct selection of the points to be coupled, and the paper proposes an innovative procedure in which a double density points subset is selected in relation to terrain characteristics. This approach reduces the complexity of the calculation and avoids that flat terrain parts, where most of the original points, are de-facto overweighed. Starting from the original dense cloud, eigenfeatures are extracted for each point and clustering is then performed to group them in two classes connected to terrain geometry, flat terrain or not; two metrics are adopted and compared for k-means clustering, Euclidean and City Block. Segmentation results are evaluated visually and by comparison with manually performed classification; ICP are then performed and the quality of registration is assessed too. The presented results show how the proposed procedure seem capable to register clouds even far apart with a good overall accuracy.
STUDY ON THE APPLICATION OF AIRBORNE LIDAR IN SEISMIC ACTIVE FAULTS IN THE NORTHERN RIM OF QINLING MOUNTAIN AND THE PIEDMONT OF HUASHAN IN CHINA
Authors: He G. , Wang A.
Published: August 2020

Abstract. Seismic active faults are closely related to earthquake disasters. Active faults determine the location and magnitude of destructive earthquakes. Therefore, strengthening active fault investigation and research is one of the main ways to reduce earthquake disasters. Airborne LiDAR, as a measurement method that can quickly obtain large-area and high-precision DEM results, can not only obtain structural distribution features of large-scale study areas on a macro scale, and determine the location and trend of active faults, but also be used to identify microstructural landforms and take researches on fine geomorphic structural features. In this paper, based on the detailed introduction, analysis and summary of the technical schemes, data processing and application of the airborne LiDAR technique in the northern margin of the Qinling Mountains and the Huashan Piedmont Fault Zone, it was demonstrated on the basic methods and technical advantages in seismic active faults and geomorphological research work of the airborne LiDAR technology, and a new and efficient method for seismic fault study was provided.
FEATURE-EXTRACTION FROM ALL-SCALE NEIGHBORHOODS WITH APPLICATIONS TO SEMANTIC SEGMENTATION OF POINT CLOUDS
Authors: Leichter A. , Werner M. , Sester M.
Published: August 2020

Abstract. Feature extraction from a range of scales is crucial for successful classification of objects of different size in 3D point clouds with varying point density. 3D point clouds have high relevance in application areas such as terrain modelling, building modelling or autonomous driving. A large amount of such data is available but also that these data is subject to investigation in the context of different tasks like segmentation, classification, simultaneous localisation and mapping and others. In this paper, we introduce a novel multiscale approach to recover neighbourhood in unstructured 3D point clouds. Unlike the typical strategy of defining one single scale for the whole dataset or use a single optimised scale for every point, we consider an interval of scales. In this initial work our primary goal is to evaluate the information gain through the usage of the multiscale neighbourhood definition for the calculation of shape features, which are used for point classification. Therefore, we show and discuss empirical results from the application of classical classification models to multiscale features. The unstructured nature of 3D point cloud makes it necessary to recover neighbourhood information before meaningful features can be extracted. This paper proposes the extraction of geometrical features from a range of neighbourhood with different scales, i.e. neighborhood ranges. We investigate the utilisation of the large set of features in combination with feature aggregation/selection algorithms and classical machine learning techniques. We show that the all-scale-approach outperform single scale approaches as well as the approach with an optimised per point selected scale.
LARGE SCALE 3D POINT CLOUD MODELING FROM CAD DATABASE IN COMPLEX INDUSTRIAL ENVIRONMENTS
Authors: Corsia M. , Chabardès T. , Bouchiba H. , Serna A.
Published: August 2020

Abstract. In this paper, we present a method to build Computer Aided Design (CAD) representations of dense 3D point cloud scenes by queries in a large CAD model database. This method is applied to real world industrial scenes for infrastructure modeling. The proposed method firstly relies on a region growing algorithm based on novel edge detection method. This algorithm is able to produce geometrically coherent regions which can be agglomerated in order to extract the objects of interest of an industrial environment. Each segment is then processed to compute relevant keypoints and multi-scale features in order to be compared to all CAD models from the database. The best fitting model is estimated together with the rigid six degree of freedom (6 DOF) transformation for positioning the CAD model on the 3D scene. The proposed novel keypoints extractor achieves robust and repeatable results that captures both thin geometrical details and global shape of objects. Our new multi-scale descriptor stacks geometrical information around each keypoint at short and long range, allowing non-ambiguous matching for object recognition and positioning. We illustrate the efficiency of our method in a real-world application on 3D segmentation and modeling of electrical substations.
AUTOMATED SEMANTIC MODELLING OF BUILDING INTERIORS FROM IMAGES AND DERIVED POINT CLOUDS BASED ON DEEP LEARNING METHODS
Authors: Gülch E. , Obrock L.
Published: August 2020

Abstract. In this paper, we present an improved approach of enriching photogrammetric point clouds with semantic information extracted from images to enable a later automation of BIM modelling. Based on the DeepLabv3+ architecture, we use Semantic Segmentation of images to extract building components and objects of interiors. During the photogrammetric reconstruction, we project the segmented categories into the point cloud. Any interpolations that occur during this process are corrected automatically and we achieve a mIoU of 51.9 % in the classified point cloud. Based on the semantic information, we align the point cloud, correct the scale and extract further information. Our investigation confirms that utilizing photogrammetry and Deep Learning to generate a semantically enriched point cloud of interiors achieves good results. The combined extraction of geometric and semantic information yields a high potential for automated BIM model reconstruction.
LOD3 BUILDING RECONSTRUCTION FROM MULTI-SOURCE IMAGES
Authors: Huang H. , Michelini M. , Schmitz M. , Roth L. , Mayer H.
Published: August 2020

Abstract. We propose a pipeline for the detection as well as modeling of individual buildings based on multi-source images. It allows to consistently reconstruct whole buildings at Level of Detail 3 (LoD3): the roof from airborne images and the facades including elements such as windows and doors mainly from terrestrial images. We employ a parametrized top-down model – the “shell model” – with the roof as well as the facades semantically and geometrically integrated. This generative model fosters stability for building detection by enabling the use of multi-source data and offers flexibility in modeling by means of a fully CAD-compatible integration of building components. Experiments performed on imagery from different terrestrial and airborne (Unmanned Aerial Vehicle – UAV) cameras demonstrate the potential of the approach.
PLAUSIBLE RECONSTRUCTION OF AN APPROXIMATED MESH MODEL FOR NEXT-BEST VIEW PLANNING OF SFM-MVS
Authors: Moritani R. , Kanai S. , Date H. , Niina Y. , Honma R.
Published: August 2020

Abstract. Structure-from-Motion (SfM) and Multi-View Stereo (MVS) are widely used methods in three dimensional (3D) model reconstruction for an infrastructure maintenance purpose. However, if a set of images is not captured from well-placed positions, the final dense model can contain low-quality regions. Since MVS requires a much longer processing time than SfM as larger amounts of images are provided, it is impossible for surveyors to wait for the SfM–MVS process to complete and evaluate the geometric quality of a final dense model on-site. This challenge results in response inefficiency and the deterioration of dense models in 3D model reconstruction. If the quality of the final dense model can be predicted immediately after SfM, it will be possible to revalidate the images much earlier and to obtain the dense model with better quality than the existing SfM–MVS process. Therefore, we propose a method for reconstructing a more plausible 3D mesh model that accurately approximates the geometry of the final dense model only from sparse point clouds generated from SfM. This approximated mesh model can be generated using Delaunay triangulation for the sparse point clouds and triangle as well as tetrahedron filtering. The approximated model can be used to predict the geometric quality of the final dense model and for an optimization-based view planning. Some experimental results showed that our method is effective in predicting the quality of the final dense model and finding the potentially degraded regions. Moreover, it was confirmed that the average reconstruction errors of the dense model generated by the optimization-based view planning went below tens of millimeters and falls within an acceptable range for an infrastructure maintenance purpose.
RESECTION AND MONTE CARLO COVARIANCE FROM VANISHING POINTS FOR IMAGES OF UNKNOWN ORIGIN
Authors: Settergren R.
Published: August 2020

Abstract. Photogrammetric analysis requires camera metadata (position, attitude, interior orientation, etc.), which is not available for all images. Modern commercial solutions for 3D reconstruction from images typically assume large amounts of purposefully-collected, highly-overlapping imagery. In this article, a system is demonstrated for recovering a 3D scene from images of unknown origin, by marking ground space axes, resecting a camera consistent with the markings, and then using the solved camera to collect 3D measurements. The capability works with close-range (vanishing points) and long-range (parallel axes) imagery. Monte Carlo analysis is used to propagate measurement uncertainty of the vanishing lines to uncertainty of exterior and interior camera parameters, which can then be used to quantify uncertainty of measurements in the 3D scene.
NETWORK ADJUSTMENT OF AUTOMATED RELATIVE ORIENTATION FOR A DUAL-CAMERA SYSTEM
Authors: Lin K. Y. , Tseng Y. H. , Chiang K. W.
Published: August 2020

Abstract. Visual odometry (VO) is a technique applied to track the dynamic positioning and orientation of a moving platform with one or more cameras taking image sequences. The determination relies on the estimation of relative orientation parameters (ROPs) of time adjacent images. The idea of stereo VO to develop a dual-camera system is adopted in this study. By taking advantage of the calibrated stereo camera, this system is able to recover the true scale of relative translation without the need from additional sensors. However, the scale might not be very accurate, and the error also could exist in the orientation including rotation and translation due to environmental factors such as the illumination and texture. Therefore, the primary objective of this study is to find the optimized theory and method of stereo VO. Through the analysis of the geometric relationship of the time adjacent stereo image pairs, locally optimized network adjustment is developed to improve the accuracy of ROPs.The proposed network adjustment model is verified by the simulation data and experiment data both. ROPs are adopted as observations that would update the states of the image sequence further. Besides, exterior orientation parameters (EOPs) of the dual-camera system could be optimized obviously during the whole operation. In this study, it is worth mentioning that 3D coordinates of object points matched in each image pair are not necessary to be calculated. The conventional bundle adjustment is not adopted, but more accurate EOPs still have been generated automatically during the process.
POSE ESTIMATION OF A MOVING CAMERA WITH LOW-COST, MULTI-GNSS DEVICES
Authors: Lourakis M. , Pateraki M. , Karolos I.-A. , Pikridas C. , Patias P.
Published: August 2020

Abstract. Without additional prior information, the pose of a camera estimated with computer vision techniques is expressed in a local coordinate frame attached to the camera’s initial location. Albeit sufficient in many cases, such an arbitrary representation is not convenient for employment in certain applications and has to be transformed to a coordinate system external to the camera before further use. Assuming a camera that is firmly mounted on a moving platform, this paper describes a method for continuously tracking the pose of that camera in a projected coordinate system. By combining exterior orientation from a known target with incremental pose changes inferred from accurate multi-GNSS positioning, the full 6 DoF pose of the camera is updated with low processing overhead and without requiring the continuous visual tracking of ground control points. Experimental results of applying the proposed method to a moving vehicle and a mobile port crane are reported, demonstrating its efficacy and potential.
KNOWLEDGE DISTILLATION USING GANS FOR FAST OBJECT DETECTION
Authors: Finogeev E. , Gorbatsevich V. , Moiseenko A. , Vizilter Y. , Vygolov O.
Published: August 2020

Abstract. In this paper, we propose a new method for knowledge distilling based on generative adversarial networks. Discriminator CNNs is used as an adaptive knowledge distilling loss. In experiments, single shot multibox detector SSD based on MobileNet v2 and ShuffleNet v1 are used as student networks. Our tests showed AP and mAP improvement of more than 3% on PascalVOC and 1% on MS Coco datasets compared with the baseline algorithm without any architecture or dataset changes. The proposed approach is general and can be used not only with SSD but also with any type of object detection algorithms.
EFFICIENT MULTI-VIEW 3D TRACKING OF ARBITRARY ROCK FRAGMENTS UPON IMPACT
Authors: Guccione D. E. , Thoeni K. , Giacomini A. , Buzzi O. , Fityus S.
Published: August 2020

Abstract. This paper presents a new methodology to accurately obtain 3D rotational velocities of blocks and fragments. Four high speed cameras are used to capture the scene. An additional two tilted mirrors are used to multiply the number of views. Hence, a total of six different viewing perspectives can be used to track translational and rotational velocities in 3D. The focus in the current work is on the rotational velocities, as tracking of the translation is generally straightforward. A common outline tracking algorithm based on the visual hull is adapted. The visual hull is further meshed using triangular elements to approximate the shape of the object. This 3D reconstruction is then used to track the 3D motion of the object. However, the accuracy of the results strongly depends on the accuracy of the 3D reconstruction which is mainly influenced by the number and position of the available views. In any case, the 3D reconstruction from the visual hull is only an approximation and significant errors can be introduced which influence the tracking accuracy. Hence, an in-house post-processing algorithm based on the knowledge of the real geometry of the object, which can generally be accurately determined after a test, was developed. The improved performance of this new post-processing method is shown by controlled spinning tests. Finally, results of a real example of an impact fragmentation test are discussed.
EVALUATION OF AIRBORNE IMAGE VELOCIMETRY APPROACHES USING LOW-COST UAVS IN RIVERINE ENVIRONMENTS
Authors: Ioli F. , Pinto L. , Passoni D. , Nova V. , Detert M.
Published: August 2020

Abstract. Traditional flow velocity measurements in natural environments require contact with the fluid and are usually costly, time-consuming and, sometimes, even dangerous. Particle Image Velocimetry allows the flow velocity field to be remotely characterized from the shift of intensity patterns of sub-image areas in at least two video frames with a known time lag. Recently, Airborne Image Velocimetry has enabled the surface velocity field of large-scale water bodies to be determined by applying Particle Image Velocimetry on videos recorded by cameras mounted on unmanned aerial vehicles. This work presents a comparison of three Airborne Image Velocimetry approaches: BASESURV, Fudaa-LSPIV and RIVeR. For the evaluation, two nadiral videos were acquired with a low-cost quadcopter. The first was recorded under low flow and seeded conditions, the second during a flood event. According to the results obtained, BASESURV is an accurate and complete research oriented approach but it is time-consuming and neither a graphical interface nor documentation are yet provided. Fudaa-LSPIV is a well-developed software package, with a user-friendly graphical interface and good documentation. However it lacks some features and the source code is closed. RIVeR may be suitable for real time monitoring thanks to the rectification of velocity vectors only. Overall, all the codes are found to be effective in performing Airborne Image Velocimetry in riverine environments.
DUAL PYRAMIDS ENCODER-DECODER NETWORK FOR SEMANTIC SEGMENTATION IN GROUND AND AERIAL VIEW IMAGES
Authors: Jiang S. L. , Li G. , Yao W. , Hong Z. H. , Kuc T. Y.
Published: August 2020

Abstract. Semantic segmentation is a fundamental research task in computer vision, which intends to assign a certain category to every pixel. Currently, most existing methods only utilize the deepest feature map for decoding, while high-level features get inevitably lost during the procedure of down-sampling. In the decoder section, transposed convolution or bilinear interpolation was widely used to restore the size of the encoded feature map; however, few optimizations are applied during up-sampling process which is detrimental to the performance for grouping and classification. In this work, we proposed a dual pyramids encoder-decoder deep neural network (DPEDNet) to tackle the above issues. The first pyramid integrated and encoded multi-resolution features through sequentially stacked merging, and the second pyramid decoded the features through dense atrous convolution with chained up-sampling. Without post-processing and multi-scale testing, the proposed network has achieved state-of-the-art performances on two challenging benchmark image datasets for both ground and aerial view scenes.
APPLICATION OF ROBOTIC SYSTEM FOR OBTAINING INFORMATION ABOUT THE AREA
Authors: Makarov A. P. , Chibunichev A. G. , Poliakova E. V.
Published: August 2020

Abstract. The Photogrammetry Department of MIIGAIK is developing the "Automated Information Generation System" (AIGS), which is designed to carry out automatic aerial survey of the object using a drone and obtain documents about the terrain (orthophotos, DMR, etc.). The system is based on a robotic mobile platform with a navigation system installed on it, a manipulator and a lifting portal. The manipulator has three degrees of freedom and is designed to manipulate the necessary when replacing automatically the battery in the UAV. The lifting portal is designed to load and fix UAVs on the platform. The robotic station is able to work both individually and as part of a network of stations. Complexes are evenly distributed on the territory where aerial photography or monitoring will be carried out. The operator is in the office, forms the job and sends it over the Internet channel, at the station. The job is run automatically on schedule.
SPRING POINT DETECTION OF HIGH RESOLUTION IMAGE BASED ON YOLOV3
Authors: Wu J. , Zhang Z. , Huang G. , Ma G.
Published: August 2020

Abstract. The Xinjiang region of China is a vast and sparsely populated area with complex topography, surrounded by basins and mountains, and its geomorphological features and water circulation process make the traditional spring water resource acquisition time-consuming, labor-consuming and inaccurate. Remote Sensing Technology has the advantages of large scale, periodicity, timeliness and comprehensiveness in target detection. In order to realize the artificial intelligence detection of springs in Xinjiang, this paper presents a method of detecting springs in remote sensing image based on the YOLOV3 network framework, based on the data set of 512 * 512 by using 0.8m remote sensing image annotation, a model of recognition of spring point based on Yolov3 network is constructed and trained. The results show that the map of spring point is 0.973, which is the basis of monitoring and protecting the natural environment in the Belt and Road Initiatives.
EFFICIENT COMPUTATION OF POSTERIOR COVARIANCE IN BUNDLE ADJUSTMENT IN DBAT FOR PROJECTS WITH LARGE NUMBER OF OBJECT POINTS
Authors: Börlin N. , Murtiyoso A. , Grussenmeyer P.
Published: August 2020

Abstract. One of the major quality control parameters in bundle adjustment are the posterior estimates of the covariance of the estimated parameters. Posterior covariance computations have been part of the open source Damped Bundle Adjustment Toolbox in Matlab (DBAT) since its first public release. However, for large projects, the computation of especially the posterior covariances of object points have been time consuming.The non-zero structure of the normal matrix depends on the ordering of the parameters to be estimated. For some algorithms, the ordering of the parameters highly affect the computational effort needed to compute the results. If the parameters are ordered to have the object points first, the non-zero structure of the normal matrix forms an arrowhead.In this paper, the legacy DBAT posterior computation algorithm was compared to three other algorithms: The Classic algorithm based on the reduced normal equation, the Sparse Inverse algorithm by Takahashi, and the novel Inverse Cholesky algorithm. The Inverse Cholesky algorithm computes the explicit inverse of the Cholesky factor of the normal matrix in arrowhead ordering.The algorithms were applied to normal matrices of ten data sets of different types and sizes. The project sizes ranged from 21 images and 100 object points to over 900 images and 400,000 object points. Both self-calibration and non-self-calibration cases were investigated. The results suggest that the Inverse Cholesky algorithm is the fastest for projects up to about 300 images. For larger projects, the Classic algorithm is faster. Compared to the legacy DBAT implementation, the Inverse Cholesky algorithm provides a performance increase by one to two orders of magnitude. The largest data set was processed in about three minutes on a five year old workstation.The legacy and Inverse Cholesky algorithms were implemented in Matlab. The Classic and Sparse Inverse algorithms included code written in C. For a general toolbox as DBAT, a pure Matlab implementation is advantageous, as it removes any dependencies on, e.g., compilers. However, for a specific lab with mostly large projects, compiling and using the classic algorithm will most likely give the best performance. Nevertheless, the Inverse Cholesky algorithm is a significant addition to DBAT as it enables a relatively rapid computation of more statistical metrics, further reinforcing its application for reprocessing bundle adjustment results of black-box solutions.
REFINEMENTS AND INTRODUCTION OF PHOTOGRAMMETRIC ELEMENTS TO AN EXPERIMENTAL TRILATERATION BASED OPTICAL METROLOGY SYSTEM
Authors: Heaps J. , Hughes B.
Published: August 2020

Abstract. OPTIMUM is a novel optical coordinate measurement system designed to determine the location of omnidirectional targets within a large volume, the previous version of the system could determine the targets location with an uncertainty of 50 × 10−6 m. This paper describes some of the limitations of the original embodiment and changes being developed to address them. Version 2 of the system aims to solve the limitations of version 1 by integrating photogrammetric processes into the design and control processes of the system, along with redesigning the mechanical and optical relationships. The redesigned instrument will result in the ability to fully automate the initial calibration process and to allow for real-time processing of the measurement algorithms.
AN AUTOMATIC ICP-BASED 2D-3D REGISTRATION METHOD FOR A HIGH-SPEED BIPLANAR VIDEORADIOGRAPHY IMAGING SYSTEM
Authors: Zhang S. , Lichti D. D. , Küpper J. C. , Ronsky J. L.
Published: August 2020

Abstract. High-Speed Biplanar Videoradiography (HSBV) is an X-ray based non-invasive imaging system that can be used to derive dynamic bony translations and rotations. The 2D-3D registration process matches a 3D bone model acquired from magnetic resonance imaging (MRI) or computed tomography (CT) scans with the 2D X-ray image pairs. This study focuses on the registration of MRI data as it can acquire detailed soft tissue contrast that cannot be easily discerned in CT scans. A novel 2D-3D registration method is reported in this paper that is suitable for the MRI-based bone models with high precision and high efficiency. In addition, an automatic initialization procedure with 64 starting poses is established to avoid user intervention in the registration. The method has been tested using the HSBV image sequence of a knee joint during walking. Thirty-five consecutive poses from the sequence were tested for the registration, and 50 non-consecutive poses randomly selected from the sequence were tested for the automatic initialization. The registration precision for each axis was 0.49 to 0.54 mm. For the initialization validation test, 48 over 50 frames were successfully initialized and two failed due to portions of the joint falling outside of the field-of-view of the system. The average time for each initialization is only about 6 min. The improved 2D-3D registration will allow determination of precise 3D kinematic parameters with high efficiency. These kinematic parameters can be used to calculate joint cartilage contact mechanics that provide insight into the mechanical processes and mechanisms of joint degeneration or pathology.
MACHINE LEARNING FOR APPROXIMATING UNKNOWN FACE
Authors: Knyaz V. A. , Kniaz V. V. , Novikov M. M. , Galeev R. M.
Published: August 2020

Abstract. The problem of facial appearance reconstruction (or facial approximation) basing on a skull is very important as for anthropology and archaeology as for forensics. Recent progress in optical 3D measurements allowed to substitute manual facial reconstruction techniques with computer-aided ones based on digital skull 3D models. Growing amount of data and developing methods for data processing provide a background for creating fully automated technique of face approximation.The performed study addressed to a problem of facial approximation based on skull digital 3D model with deep learning techniques. The skull 3D models used for appearance reconstruction are generated by the original photogrammetric system in automated mode. These 3D models are then used as input for the algorithm for face appearance reconstruction. The paper presents a deep learning approach for facial approximation basing on a skull. It exploits the generative adversarial learning for transition data from one modality (skull) to another modality (face) using digital skull 3D models and face 3D models. A special dataset containing skull 3D models and face 3D models has been collected and adapted for convolutional neural network training and testing. Evaluation results on testing part of the dataset demonstrates high potential of the developed approach in facial approximation.
AUTOMATIC TUMULI DETECTION IN LIDAR BASED DIGITAL ELEVATION MAPS
Authors: Németh Z. , Benedek C.
Published: August 2020

Abstract. In this study, we aim to identify Iron Age tumuli with a probabilistic method, in digital terrain models extracted from airborne LiDAR measurements. Our task was particularly challenging due to the strong and uneven elevation of the ground where tumuli are located. In addition, the area is densely covered by vegetation, mainly by forests, which makes it difficult to create a surface model. The difficulty was exacerbated by the fact that the height and width of tumuli is very diverse but incomparably smaller than the extension of the area. In contrast to the various visualization techniques (openness, local relief model) used since decades for identifying tumuli, our method recognises these features automatically. Our approach is based on a Marked Point Process Model (MPP), which due to its inverse methodology, allows us to detect tumuli with high precision, especially in comparison with bottom-up methods. We quantitatively evaluated the method on six test data sets and we demonstrated its advantages compared to a traditional Hough transform-based method.
GEOMATIC CONTRIBUTION FOR THE RESTORATION PROJECT OF THE VALENTINO CASTLE GREEN ROOM. FROM DATA ACQUISITION TO INTEGRATED DOCUMENTATION
Authors: Patrucco G. , Chiabrando F. , Dameri A. , Teppati Losè L.
Published: August 2020

Abstract. In the framework of restoration operations, valuable assistance can be supplied from innovative techniques and methods developed in the field of Geomatics. Over the years, this continuous collaboration has produced synergistic and interdisciplinary results that have been successfully contributing to heritage conservation and valorisation. In the case of the current research, thorough multisensory investigations have been performed in order to provide a deeper knowledge of the Green Room of the Valentino Castle in Turin and to support the planning of the future restoration works that will involve this valuable asset. In the framework of this experience, four LiDAR systems have been employed in order to evaluate the different results obtainable from the sensors. Additionally, a complete photogrammetric close-range survey has been carried out, and some tests were completed using a hyperspectral camera. The workflow followed during the current research is described in this paper, and a comparison between the obtained outputs is proposed, focusing on the characteristics of these metric products, useful and sometimes necessary in the framework of the restoration project. Besides, some considerations on the advantages and the issues connected with the use of these reality-based data as a starting point for HBIM (Heritage Building Information Modeling) model generation are proposed, along with some observations about the potentialities of a photogrammetric co-registration approach using spectrum technologies for deterioration/decay detection and monitoring of heritage.
QUANTIFYING DEPTH OF FIELD AND SHARPNESS FOR IMAGE-BASED 3D RECONSTRUCTION OF HERITAGE OBJECTS
Authors: Webb E. K. , Robson S. , Evans R.
Published: August 2020

Abstract. Image-based 3D reconstruction processing tools assume sharp focus across the entire object being imaged, but depth of field (DOF) can be a limitation when imaging small to medium sized objects resulting in variation in image sharpness with range from the camera. While DOF is well understood in the context of photographic imaging and it is considered with the acquisition for image-based 3D reconstruction, an “acceptable” level of sharpness and associated “circle of confusion” has not yet been quantified for the 3D case. The work described in this paper contributes to the understanding and quantification of acceptable sharpness by providing evidence of the influence of DOF on the 3D reconstruction of small to medium sized museum objects. Spatial frequency analysis using established collections photography imaging guidelines and targets is used to connect input image quality with 3D reconstruction output quality. Combining quantitative spatial frequency analysis with metrics from a series of comparative 3D reconstructions provides insights into the connection between DOF and output model quality. Lab-based quantification of DOF is used to investigate the influence of sharpness on the output 3D reconstruction to better understand the effects of lens aperture, camera to object surface angle, and taking distance. The outcome provides evidence of the role of DOF in image-based 3D reconstruction and it is briefly presented how masks derived from image content and depth maps can be used to remove unsharp image content and optimise structure from motion (SfM) and multiview stereo (MVS) workflows.
3D DOCUMENTATION OF CULTURAL HERITAGE: THE CASE STUDY OF BANTEAY SREI TEMPLE IN ANGKOR, SIEM REAP
Authors: Zhang L. , Wang F. , Cheng X. , Li C. , Lin H. , Song Y.
Published: August 2020

Abstract. 3D documentation and visualization of cultural heritage has a great significance in preserving the memories and history, and supports cultural tourism. It is of great importance to study the 3D reconstruction of cultural relics and historic sites. Preservation, visualization of valuable cultural heritage has always been a difficult challenge. With the developments of photogrammetry, terrestrial laser scanning, 3D models were able to obtained quickly and accurately. In this paper we present the survey and 3D modelling of an ancient temple, Banteay Srei, situated in Angkor, which has long been admired as a “Precious Gem” of Khmer Art for its miniature size of structures and exceptional refinement of the sculptures. The survey was performed with FARO Focus3D 330 and FARO Focus3D 120 terrestrial laser scanners, a micro unmanned aerial vehicle (UAV) (DJI Phantom 4 Pro) and a digital camera (Nikon D90). Once the acquired scans were properly merged, a 3D model was generated from the global point cloud, and plans, sections and elevations were extracted from it for restoration purposes. A short multimedia video was also created for the “Digital Banteay Srei”. In the paper we will discuss all the steps and challenges addressed to provide the 3D model of Banteay Srei Temple.
PORTING ARDUPILOT TO ESP32: TOWARDS A UNIVERSAL OPEN-SOURCE ARCHITECTURE FOR AGILE AND EASILY REPLICABLE MULTI-DOMAINS MAPPING ROBOTS
Authors: Beaudoin L. , Avanthey L. , Villard C.
Published: August 2020

Abstract. In this article, we are interested in the implementation of an open-source low-level architecture (critical system) adapted to agile and easily replicable close-range remote sensing robots operating in multiple evolution domains. After reviewing the existing autopilots responding to these needs, we discuss the available hardware solutions and their limits. Then, we propose an original solution (software and hardware) that we developed to obtain a universal low-level architecture for all our exploration robots, whatever their environment of evolution, and the steps needed to make it run on our chosen family of micro-controllers: the ESP32. Finally, we present the operational results obtained on our different platforms (land, surface, submarine and air), their limits and the envisaged perspectives.
SEAFLOOR MAPPING FROM MULTISPECTRAL MULTIBEAM ACOUSTIC DATA AT THE EUROPEAN OPEN SCIENCE CLOUD
Authors: Mertikas P. , Karantzalos K.
Published: August 2020

Abstract. Recent technological advances in the underwater sensing instrumentation provide currently active multibeam echosounders that can acquire backscatter observations from multiple spectral frequencies. In this paper, the main objective was to design, develop and validate an efficient and robust multispectral, multibeam data processing framework including advanced machine learning tools for seabed classification. In order to do so, we have integrated different machine learning tools like support vector machines and random forests towards the classification of seabed classes. We have performed extensive experiments with different splitting ratios, regarding training and testing sets, in order to assess possible overfitting. The entire pipeline has been implemented in a scalable containerized manner in order to be deployed in cloud infrastructures and more specifically at the European Open Science Cloud. Experimental results, the performed qualitative and quantitative evaluation along with the comparison with the state of the art indicated the quite promising potential of our approach.
Wari’: conversão, identidade cultural e marcadores territoriais na Terra Indígena Igarapé Laje em Rondônia
Authors: Silva Adnilson de Almeida , Santana Francisco Marcolino , Silva Josué da Costa
Published: August 2020

O presente artigo procura analisar o modo de atuação das missões evangelizadoras na Aldeia Laje Velho, especificamente conduzidas pelas Missões Novas Tribos do Brasil - MNTB, que aproximadamente há cinquenta anos dedicam-se em converter esse coletivo indígena à religiosidade cristã. Outro aspecto aqui analisado é a identidade cultural Wari’, suas experiências ancestrais, sua cosmogonia, territorialidade e todo seu aporte simbólico com a natureza. Em seguida relacionamos esses dois aspectos aqui mencionados com os marcadores territoriais estruturantes e estruturadores, de modo que procuramos identificar e distinguir quais ações são consideradas originárias à coletividade e quais ações são características da sociedade envolvente. O trabalho é resultante de uma atividade de campo realizada com o povo Wari’ na Terra Indígena Igarapé Laje, localizada entre os municípios de Guajará - Mirim e Nova Mamoré no Estado de Rondônia. Apresenta em seu contexto as percepções obtidas e suas respectivas análises alicerçadas em abordagens metodológicas construídas por meio de referenciais bibliográficos, entrevistas, em que a fenomenologia constitui-se como base principal às análises em questão.

PALAVRAS – CHAVE: Conversão. Identidade cultural. Marcadores Territoriais. Wari’.
Agronegócio, campesinato e as especificidades da territorialização do capital em Goiás
Authors: Calaça Manoel , Misnerovicz José Valdir
Published: August 2020

Este artigo reflete os avanços recentes do agronegócio em Goiás e o caráter concentrador e seletivo desse processo. O objetivo principal foi demonstrar que as políticas de financiamento do conjunto de atividades que compõem o agronegócio sustentam a expansão ocorrida no estado nas últimas décadas em oposição à expansão da produção camponesa. Para sustentar as reflexões apresentadas, foram analisados dados referentes à produção agrícola, bem como a destinação de recursos públicos para o financiamento das agroindústrias, que são indutoras da expansão da produção de commodities no estado de Goiás. Fica evidente o caráter concentrador dessas políticas. Concentram-se recursos nos setores hegemônicos em relação ao campesinato, bem como a concentração dos investimentos no centro-sul goiano, aumentando as desigualdades regionais e sociais.

Palavras chaves: Agronegócio. Campesinato. Financiamento. Concentração.
Lazer em Altamira-PA e as transformações após a construção da Usina Hidrelétrica de Belo Monte
Authors: Mendes Francivaldo José da Conceição , Martins de Sousa César Augusto , Queiroz de Miranda Neto José
Published: August 2020

Esta pesquisa investigou as manifestações do lazer na cidade de Altamira-PA, considerando as transformações  urbanas  advindas da implementação da Usina Hidrelétrica “Belo Monte” (UHBM). De maneira geral, descreveu-se as principais práticas de lazer existentes na cidade de Altamira, considerando a relevância da implementação da UHBM como evento indutor de um considerável reordenamento na lógica da (re)produção dos espaços de lazer. As entrevistas abertas e a observação participante constituíram-se em estratégias metodológicas dessa pesquisa, na medida em que possibilitaram uma melhor compreensão da realidade do lazer de Altamira. Os resultados evidenciaram um forte processo de (re)definição dos espaços de lazer, caracterizados pela ação de agentes, instituições, Estado, e dos sujeitos residentes na cidade. A realidade emergente evidencia uma nova dinâmica dos espaços e das mútiplas práticas de lazer no advento da UHBM. Tem destaque nesse cenário, a emergência de novos arranjos na “territorialidade do lazer”, caracterizados pelas novas formas estabelecidas pelos sujeitos, destacando-se o aumento dos espaços privados de lazer em comparação aos públicos.

Palavras-Chave: Lazer, Hidrelétrica Belo Monte, Altamira-PA.