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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For each example, Figure 18 a shows some of the images used for geconstruction reconstruction. Then, after depth—map refinement and depth—map fusion, a dense 3D point data cloud can be obtained.

With the continuous development of computer hardware, multicore technologies, and GPU technologies, the SfM algorithm can now be used in several areas.

Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera

In order to complete the uncailbrated 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.

Finally, all depth maps are fused to generate dense 3D point cloud data.

Thus, there is an urgent need to reconstruct 3D structures viveo the 2D images collected from UAV camera. Green points represent the positions of camera, and red points are control points, white points are structural feature points. Thesecontributions are presented as appended papers to enable theexperienced reader to easily study the novelty of the thesis. The main text gives a detailed coherent account of thetheoretical foundation for the system and its components.

It is assumed that the images used for reconstruction are frm in texture. MicMac—A free, open-source solution for photogrammetry. There must be at least four feature points, and the centroid of these feature points can then be calculated as follows:.


As the number of images and their resolution increase, the computational times of the algorithms will increase significantly, limiting them in some high-speed reconstruction applications. The total number of images in C is assumed to be N. Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable.

And the number of points in point cloud is 4, In this case, the UAV flight is over a botanical garden.

Automatic Dense Reconstruction from Uncalibrated Video Sequences

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.

A variety of SfM strategies have emerged, including incremental [ 78 ], hierarchical [ 9 ], and global [ 101112 ] approaches. 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.

In many applications, the SfM algorithm has higher requirements for the computing speed and accuracy. First, a principal component analysis method of the feature points is used to select the key images suitable for 3D reconstruction, which erconstruction that the algorithm improves the calculation speed with almost no loss of accuracy.

Author Contributions Yufu Qu analyzed the weak aspects of existing methods and set up the theoretical framework. One motivation is to make it possible forany amateur photographer to produce graphical models of theworld with the use of a computer.

Open in a separate window. Kinds of improved SLAM algorithms have been proposed to adapt to different applications. Second, these key images are inserted into a fixed-length image queue. We obtain the correspondence M: There are two aspects that affect the speed of the algorithm. Considering the continuity of the images taken by UAV camera, this paper proposes a 3D reconstruction method based on an image queue.


By using Delaunay triangulation, we can obtain the mesh data from the 3D feature points.

Urban 3D Modelling from Video

Distance histograms in Figure 9 a—c is statistics results of distance point cloud in Figure 8 a—c. With the rise of artificial intelligence research, the parameters of m and k can be selected automatically by using deep learning and machine learning.

These algorithms can obtain reconstruction results with an even higher density and accuracy. The algorithm flowchart is outlined in Figure 1. 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. After using a fixed-size image queue, the global structure calculation is divided into several local structure calculations, thus improving the speed of the algorithm with almost no loss of accuracy.

MVS When the positions and orientations of the cameras are known, the MVS algorithm can reconstruct the 3D structure of a scene by using multiple-view images.

After that, a dense point data cloud and mesh data cloud can be obtained. When processing weakly textured images, it reconstfuction difficult for this method to generate a dense point cloud. Positions and orientations of monocular camera and sparse point map can be obtained from the images by using SLAM algorithm. In the first experiment.