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 accuracy of the algorithm is determined by calculating the nearest neighbor distance of the two point clouds [ 28 ]. This task is frequently carried outin movie making but is then performed with a great deal bideo manual work. The results of experiment images used in this paper are present in Figure 13Figure 14Figure 15Figure 16Figure 17 and Figure Theory and Practice; Automaatic, Greece.

However, as the requirements have grown and matured, 2D images have not been able to meet the requirements of many applications such as three-dimensional 3D terrain and scene understanding.

When we use bundle adjustment to optimize the parameters, we must keep the control points unchanged or with as little change as possible. The flight distance is around m.

Automatic Dense Reconstruction from Uncalibrated Video Sequences | Open Library

The total number of images in C is assumed to be N. By carrying a digital camera on a UAV, two-dimensional 2D images can be obtained.

In the first experiment. Principal Component Analysis PCA is used to analyze the correlation of features over frames to automate the key frame selection. This is achieved by weighting the error term of the control points. The running times of the algorithm are recorded in Table 2and the precision is 1 s.

They both estimate the localizations and orientations of camera and sparse features. The flight distance is around vidwo m.


The patch-based matching method is used to match other pixels between images. The structural calculation of the images in the queue is then repeated until all images are processed. The calculation of distance is performed only on the common part of the two point clouds. Eventually, we will complete the structural calculation of all images by repeating the structural computation and queue update.

Automatic Dense Reconstruction from Uncalibrated Video Sequences

In order sequencces select the key images suitable for 3D reconstruction, the principal component points are used to estimate the interrelationships between images. The flight blocks are integrated for many parallel strips.

The distance point clouds are shown in Figure 8 a—c. The following matrix is formed by the image coordinates of the feature points:.

For aautomatic feature point matching algorithms, all images must match each other; thus, the time complexity of matching is O N 2.

Xuan Zhang collected the experimental image data and helped improving the performance of the algorithm and analyzed the result. Figure 4 illustrates the process of the algorithm. This thesis describes a system that completely automaticallybuilds a three-dimensional model of a scene given a sequence ofimages of the scene. Please review our privacy policy.

The fundamental matrix of the two images is obtained by the random sample auomatic RANSAC method [ 22 ], and the essential matrix between the two images is then calculated when the intrinsic matrix obtained by the calibration method proposed in [ 23 ] is known. Figure 6 d,e present some of the standard images [ 28 ] reconstruvtion by a camera fixed to a robotic arm with known positions and orientations which is provided by roboimagedata.

The problem addressed in this step reconshruction generally referred to as the MVS problem.

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In addition, a high precision New-mark Systems RT-5 turntable is used to provide automatic rotation of the object. Author Contributions Yufu Qu analyzed the weak aspects of existing methods and set up the theoretical framework. The proposed method divides the global bundle adjustment, which optimizes a large number of parameters, into several local bundle adjustments so that the number of the parameters remains small and the calculation speed of the algorithm improves greatly.


The structure of the images in C r is known, and the structural information contains the coordinates of the 3D feature points marked as P r. Literature Review The general 3D reconstruction algorithm without a priori positions and orientation information can be roughly divided into two steps. Selecting Key Images In order to complete the dense reconstruction of the point cloud and improve the computational speed, the key images which are suitable for the structural calculation must first be selected from a large number of UAV video images captured by a camera.

Table 2 Running Time Comparison. The new image must meet the following two conditions. Finally, a dense 3D point cloud can be obtained using the depth—map fusion method. Author information Article notes Copyright and License information Disclaimer.

Figure 10 c the number of points in point cloud generated by MicMac isAll of the images in the image queue are recorded as C qreconstructioj the structure of all of the images in C q is calculated.

An implementation of this method can be found in the open-source software openMVS [ 16 ]. Finally, dense 3D point cloud data of the scene are obtained by using depth—map fusion. Precision Evaluation In order to vidwo 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 with the standard point cloud PC STL which is captured by structured light scans The RMS reconstructikn of all ground truth poses is within 0.