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4: An intermediate iteration in face sequence, when using an inward force, G. It is seen how the surface has 'gone through' the true surface, S * . In later iterations the holes will get bigger, and eventually the smoothness constraint of (11.3) will pull the surface from S * , whereupon it will collapse under its own curvature.
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Zur flächigen Bestimmung der Wasseroberflächentemperatur wurde für angewandte Fragestellungen der Bundesanstalt für Gewässerkunde (BfG) eine Messkampagne an der Hahnöfer Nebenelbe durchgeführt. Aufgrund der fachlichen Anforderungen und Charakteristika des Messgebietes wurde ein Tragschrauber eingesetzt. Dieser wird vom Anwendungszentrum für multimo...
Die Bildqualität ist eine der bestimmenden Größen für die erreichbaren Höhengenauigkeiten bei der photogrammetrischen Bestimmung der Geländeoberfläche, insbesondere bei Verfahren wie dem Structure from Motion (SfM). Die einzusetzenden Kamerasysteme für Unmanned Aerial Systems (UAS) weisen unterschiedliche Sensorkonzepte auf, die sich auf die Bildqu...
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... SFM is the problem of reconstructing 3D images from 2D images. SFM solves two main problems (Aanaes, 2003), as shown in Figure 1. The first is the surveying of an unknown structure from known camera positions. ...
... Following the structure estimation based on the features, an estimate of the camera parameters, and the 3D structure of the extracted features are at hand. If the extracted features are image points, then the estimated 3D structure is a 3D point cloud (Aanaes, 2003). According to the data used 3D reconstruction approaches can be classified as feature-based or flow-based techniques. ...
Structure from motion (SFM) is the problem of reconstructing the 3D image from 2D images. The main problem of 3D reconstruction is the quality of the 3D image that depends on the number of 2D slices input to the system. A large number of 2D slices may lead to high processing time. This paper introduces a new model to reconstruct the 3D image from any 2D image by using marching cubes algorithm. We use the LabVIEW program to build the system and use the Biomedical Toolkit to read and registered any 2D images. Our main goal is to implement the 3D reconstruction system to produce a high-quality 3D image with a minimum number of 2D slices and to decrease the execution time as possible. We apply our system on two datasets; all the experimental results have proved the efficiency and effectiveness of this system in 3D image reconstruction from any 2D image type. As shown in results, changing iso_value, image type and a number of images, affects the quality of 3D image reconstruction, and the processing time. Reference to this paper should be made as follows: Shalaby, A., Elmogy, M. and Elfetouh, A.A. (2019) '3D image reconstruction from different image formats using marching cubes technique', Int.
... SFM is the problem of reconstructing 3D images from 2D images. SFM solves two main problems (Aanaes, 2003), as shown in Figure 1. The first is the surveying of an unknown structure from known camera positions. ...
... Following the structure estimation based on the features, an estimate of the camera parameters, and the 3D structure of the extracted features are at hand. If the extracted features are image points, then the estimated 3D structure is a 3D point cloud (Aanaes, 2003). According to the data used 3D reconstruction approaches can be classified as feature-based or flow-based techniques. ...
Structure from motion (SFM) is the problem of reconstructing the 3D image from 2D images. The main problem of 3D reconstruction is the quality of the 3D image that depends on the number of 2D slices input to the system. A large number of 2D slices may lead to high processing time. This paper introduces a new model to reconstruct the 3D image from any 2D image by using marching cubes algorithm. We use the LabVIEW program to build the system and use the Biomedical Toolkit to read and registered any 2D images. Our main goal is to implement the 3D reconstruction system to produce a high-quality 3D image with a minimum number of 2D slices and to decrease the execution time as possible. We apply our system on two datasets; all the experimental results have proved the efficiency and effectiveness of this system in 3D image reconstruction from any 2D image type. As shown in results, changing iso_value, image type and a number of images, affects the quality of 3D image reconstruction, and the processing time.
... Computer Vision due to its flexible implementation, academically challenging algorithm and commercial prospects (Aanaes, 2003 Bartoš et al. (2014). ...
... By applying the so-called Structure from Motion (SfM) technique, the overlap of 2D digital photographs can be used to create 3D point-cloud data from different viewpoints [6]. To get a better representation of topographic data, a new multi-view approachhasbeen proposedin this work. ...
Top-view data obtained from LiDAR systems has long been used as topographic input data for urban flood modelling applications. This high-resolution input data has considerable potential to improve urban flood modelling predictions with more detail. However, the difficulty of employing top-view data is that it may create some missing urban features because this type of data cannot represent any urban features, which are hidden underneath other objects. These hidden features may play a substantial part in diverting floodwater flowing through, especially in complex urban areas. The recent advances in Photogrammetry and Computer Vision techniques offer an opportunity to create high-resolution topographic data. By using a consumer digital camera, 2Ddigital photo scan is taken from different viewpoints. The so-called Structure from Motion (SfM) technique can use these overlapping photos and reconstruct theminto3D point-cloud data with a high level of accuracy and resolution, applying such using a cost effective approach. In this work, we create street-view SfM point-cloud data obtained from street viewpoints. We also introduce a new multi-view approach by merging top-view LiDAR data with street-view SfM data. This new multi-view data can be used as topographic input data for a coupled 1D-2D applying such new data, the flood simulation results can highlight some flood propagations much better than using the traditional top-view LiDAR data. Therefore, it has the potential to enhance the multi-view approach into practicable flood-modelling applications for the present and future urbanising areas.
... To have a more proper definition for structure from motion is: "The estimation of the 3D structure of a usually rigid object and relative camera motion from 2D images hereof, when the external camera parameters are unknown but translating" [9]. It should be noted, that from now and then, the camera parameters are unknown also, so that it's inflexibility limitation shall be relaxed. ...
Augmented Reality means interacting with the real world circumstances and virtual objects at the same time which is augmented by system generating inputs. That is, it allows the system generated content to be superimposed over a live camera of the real world. It is an absolutely graphical experience that is combined with real world circumstances. Here, the landmark will be acquired as an input of the image by sight-based camera parameter estimation methods by using SFM (STRUCTURE FROM MOTION) technique. In this paper, we are going to overlay real still images of landmark with virtual computer graphical images. These SFM techniques which have been discussed earlier in some of the previous papers will not work in real time applications because of its high cost. But here, this method will achieve fast and accurate landmark estimation based on SFM and we obtain the 3-D structure of a landmark by using this technique which has no need of information about the scene.
... Significant work has been done on multiple viewpoint reconstruction and extrinsic calibration of single or multi camera systems (see e.g. [15], [16]). Registering multiple range images has also been studied extensively (e.g. ...
In this paper we present an algorithm for joint extrinsic calibration of a moving sensor device consisting of a low cost industrial 2D laser range finder and a single high quality consumer DV video camera mounted on a camera crane. The calibration is based on the minimization of the Euclidean projection error of scene points in many frames captured at different viewpoints. The cali-bration procedure is designed to support any arbitrary motion trajectory of the acquisition de-vice. In this work the capture geometry for con-centric mosaics, a well known image-based ren-dering technique, is used to evaluate the perform-ance of the proposed algorithm. The projection error after calibration is less than one pixel on average.
... Further more, the feature matching is error prone, and occlusion in the images makes some of the information unreliable. The problem can be addressed to make a more robust factorization, an example of such research can be found in [8]. ...
... The result on the distribution of using auto-adjusted constants is illustrated, for the first 200 proposals, in figure 12. 8. All constants start out using the ...
... This datasets is very similar to the second dataset used in Vogiatzis article. As mentioned in 8 The result can be seen in figure 12.13, which shows a clear difference between the annealing schedules. The exponential schedule starts in the lead however it is closely followed by the linear method. ...
The advantage from aerial-base provides details of the terrain and the land covers from top viewpoint, which may be the best choice to represent the whole area. However, from the top viewpoint in a dense urban area, some details of building ground-footprints have been lost or hidden by roofs or constructions itself. These break-line footprints are often required for setting up the surface flow modelling. Thus, for the rapidly growth urban areas which especially required for an up-to-date terrain and land covers, the results of the surface modelling may cause a fault flow route and direction which will also create severe problem in pathway delineation. In contrast, using the advantage from multidimensional views will provide the terrain and land covers at high accuracy details in space and time, the limitation mentioned above can be efficiently treated.
This paper presents the developments of overland flow modelling for analysis of urban flooding. The input datasets for setting up the surface flow model have generated semi- or fully-automatic process using detailed analysis of Digital Terrain Model (DTM) and Digital Surface Model (DSM) to represent terrains and covered constructions in the urban areas. The emphasis is placed on detailed sensitivity analysis of ponds and the preferential pathways creation.
The DSMs will be generated, with high-resolution and precise accuracy in a global coordinate system, by using bundle adjustment techniques for integrating the images from multidimensional views. Then DTMs will be reconstructed by using filtering techniques. The DTMs and DEMs datasets, from the original LiDAR and the integrated images from multidimensional view, are generated in the same catchment, which will be considered in order to compare the results obtained using the methodology. Discussion details about flood depth, flood extent, and flood velocity in urban areas as a function of flood risk assessment are given.
We present a novel approach to improve 3D structure estimation from an image stream in urban scenes. We consider a particular setup where the camera is installed on a moving vehicle. Applying traditional structure from motion (SfM) technique in this case generates poor estimation of the 3d structure due to several reasons such as texture-less images, small baseline variations and dominant forward camera motion. Our idea is to introduce the monocular depth cues that exist in a single image, and add time constraints on the estimated 3D structure. We assume that our scene is made up of small planar patches which are obtained using over-segmentation method, and our goal is to estimate the 3D positioning for each of these planes. We propose a fusion framework that employs Markov Random Field (MRF) model to integrate both spatial and temporal depth information. An advantage of our model is that it performs well even in the absence of some depth information. Spatial depth information is obtained through a global and local feature extraction method inspired by Saxena et al. [1]. Temporal depth information is obtained via sparse optical flow based structure from motion approach. That allows decreasing the estimation ambiguity by forcing some constraints on camera motion. Finally, we apply a fusion scheme to create unique 3D structure estimation.
Using physically based computational models coupled with remote sensing technologies, photogrammetry techniques, and GIS applications are important tools for flood hazard mapping and flood disaster prevention. Also, information processing of massive input data with refined accuracy allows us to develop and to improve urban-flood-modeling at a detailed level. The topographical information from digital surface model (DSM) or digital terrain model (DTM) is essential for flood managers who actually require this high accuracy and resolution of input data to set up their practical applications. Light detecting and ranging (LiDAR) techniques are mainly used, but these costly techniques can be appraised by equipments, maintenance, and operations which include aircraft. Recent advances in photogrammetry and computer vision technologies like structure form motion (SfM) technique are widely used and offer cost-effective approaches to reconstruct 3D-topographical information from simple 2D photos, so-called 3D reconstruction. In terms of input data for flood modeling, the SfM technique can be comparable to other acquisition-techniques. In this paper, there are one experimental and two case studies. Firstly, a result of the experiment showed a similarity between flood maps by applying the SfM process form the 3D-reconstruction and using benchmark information. These 3D-reconstruction processes started from 2D photos, which were taken from virtual scenes by using multidimensional-view approach. These photos can be used to generate 3D information which is later used to create the DSM from multidimensional fusion of views (MFV-DSM). Then, the DSM was used as input data to set up 2D flood modeling. Thereafter, when using the DSMs as topographical input data, comparison between a benchmark DSM and MFV-DSM shows similarity flood-map results in both flood depths and flood extends. Secondary, the two cases from real world scenes also showed possibilities of using the SfM technique as an- alternative acquisition tool, providing 3D information. This information can be used as input data for setting up modeling and can possibly be comparable or even outcompete with other acquisition techniques, such as LiDAR. As a result, using the SfM technique can be extended to become promising methods in practicable applications for modeling real flood events in real world scenes.
