AUTOMATIC DENSE RECONSTRUCTION FROM UNCALIBRATED VIDEO SEQUENCES PDF

Automatic Dense Reconstruction from Uncalibrated Video Sequences. Front Cover. David Nistér. KTH, – pages. Automatic Dense Reconstruction from Uncalibrated Video Sequences by David Nister; 1 edition; First published in aimed at completely automatic Euclidean reconstruction from uncalibrated handheld amateur video system on a number of sequences grabbed directly from a low-end video camera The views are now calibrated and a dense graphical.

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The algorithm first obtains the feature points in the structure calculated by the SfM.

Maxime Lhuillier’s home page

The SfM algorithm is used to obtain the structure of the 3D scene and the camera motion from the images of stationary objects. As is shown in Figure 18 c, the 3D point cloud is generated by depth—map fusion. Without the use of ground control fense, the result of our method lost the accurate scale of the model.

The accuracy of our result is almost the same as result of openMVG and MicMac, but the speed of our algorithm is faster than them. The process is illustrated in Figure 3.

Some of them are used for vision-based navigation and mapping. Precision Evaluation In order to test the accuracy of the 3D point cloud data obtained by the algorithm proposed in this study, we compared the point cloud generated by our algorithm PC reconstructiion the standard point cloud PC STL which is captured by structured light scans The RMS error of all ground truth poses is within 0.

Two images are selected from the queue as the initial image pair using the method proposed in [ 21 ]. Kinds of improved SLAM algorithms have been proposed to adapt to different applications.

Urban 3D Modelling from Video

Considering the continuity of the images taken by UAV camera, this paper proposes a 3D reconstruction method based on an image queue. The distance point clouds are shown in Figure 8 a—c. The main text gives a detailed coherent account of thetheoretical foundation for the system sequencex its components. Finally, dense point cloud data can be obtained by fusing these depth maps. Finally, a dense 3D point cloud can be obtained using the depth—map fusion method.

Thus, there is an urgent need to reconstruct 3D structures from the 2D images collected from UAV camera. After remapping the pixels onto new locations on the image based on distortion model, the image distortion caused autpmatic lens could be eliminated. Introduction Because of the rapid development of the unmanned aerial vehicle UAV industry in recent years, civil UAVs have been used in agriculture, energy, environment, public safety, infrastructure, and other fields.

Different color means different value of distance. In order to test the accuracy and speed of the algorithm proposed in this study, real outdoor photographic images taken from a camera fixed on a UAV and standard images together with standard point cloud provided by roboimagedata [ 27 ] are used to reconstruct various dense 3D point clouds. Results that have been produced from realworld sequences acquired with a handheld video camera arepresented. The number of control points is k.

The accuracy of the algorithm is determined by calculating the nearest neighbor distance of the two point clouds [ 28 ]. Equation 9 is the reprojection error formula of the weighted bundle adjustment. And d is standard point cloud provided by roboimagedata.

On an independent thread, the depth maps of the images are calculated and saved in the depth-map set. They both achieved state-of-the-art results. The following matrix is formed by the image coordinates of the feature points:. Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable.

The final results accurately reproduce the appearance of the scenes. There must be at least four feature points, and the centroid of these feature points can then be calculated as follows:.

With this structural information, the depth maps of these images can be calculated. The method proposed by Shen [ 16 ] sequencees one of the most representative approaches. Table 2 Running Time Comparison.

Maxime Lhuillier’s home page.

A paradigm for model fitting with applications to image analysis and automated cartography. Key images selection is very important to the success of 3D reconstruction. Our method divides a global bundle adjustment calculation into several local bundle adjustment calculations, greatly improving the calculation speed of the algorithm and making the structures continuous. When reconsteuction weakly textured images, it is difficult for this method to generate a dense point cloud.

Our future work will be aimed at cumulative errors elimination and will uncslibrated higher accuracy. This method can easily and rapidly obtain a dense point cloud.

An implementation of this method can be found in the open-source software openMVS [ 16 ]. Finally, all depth maps are fused to generate dense 3D point cloud data. Distance histograms in Figure 9 a—c is statistics results of distance point cloud in Figure 8 a—c. They both estimate the localizations and orientations of camera and sparse features. With the help of feature point matching, bundle adjustment, and other technologies, Snavely completed the 3D reconstruction of objects by using images of famous landmarks and cities.

Most SLAM algorithms are based on iterative nonlinear optimization [ 12 ].

Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera

As is shown in Table 2. The selected key images should have a good overlap of area for the captured scenes. After the above steps, the structural calculation of all of the images in C q can be performed. To ensure the smooth of two consecutive point cloud, an improved bundle-adjustment named weighted bundle-adjustment is used in this paper. The first two terms of radial and tangential distortion parameters are also obtained and used for image rectification.

The flight distance is around 50 m. Finally, dense 3D point cloud data of the scene are obtained by using depth—map fusion. Among these theories and methods, the three most important categories are the simultaneous localization and mapping SLAM [ 123 ], structure from motion SfM [ 4567891011121314 ] and multiple view stereo MVS algorithms [ 151617 ], which have been implemented in many practical applications.

The problem addressed in this step is generally referred to as the MVS problem. To compress a large number of feature points into three PCPs Figure 2 b .