Research On Feature Based 3D Scene Reconstruction Techniques From Image Sequence

The recovery of structures of the scene and motion of the cameras from image sequence is one of the most important applications in computer vision. There are two kinds of methods of the 3d scene reconstruction: one is based on features, the other is based on optical flows. This paper researches the technology of 3d scene reconstruction from image sequence based on feature.An accelerated robust parameter estimation strategy named MLESAC-A is presented. By set preview and post verification, not only can it filters degenerate samples, but also can it adopts dynamic sample method so that it can improve the speed of the algorithm. Experiment on synthesis images show that MLESAC-A is much faster than MLESAC, and when the proportion of outliers is lower than 30% the consumed time of RANSAC, MLESAC-A(EM) and MLESAC-A(ME) is almost the same, but when the proportion is higher than 30% the time of RANSAC increases exponentially while the time of MLESAC-A(EM) and MLESAC-(ME) increase indistinctively, this shows MLESAC-A is more stable than RANSAC.A conclusion that traditional robust strategy like RANSAC is based on 1D data is pointed out, this will restrict the number of correspondence so that the quality of reconstruction will be reduced. A MLESAC strategy based on 2D data is advanced, it uses matched number of each group of correspondence and matched score to guide sample process. Experiments on simple scene and complex scene show that the number of correspondence has been increased by 16.7% and 56.8% respectively.A simplified method that implements triple-view geometry constraint is put forward to substitute for the computation of trifocal tensor. By global features matching among three views, it can obtains maximum number of correspondence, and by the limitation of error matrix, the efficiency of the global matching process will be improved.A parallel iterative hierarchical projective reconstruction strategy is presented, which is organized on the reconstruction unit of triple-view. The usage of three view geometry constraint can avoid the uncertainty in epipolar geometry constraint which is used in traditional iterative reconstruction. The parallel method combines continues reconstruction unit until there is only one unit left. For a sequence of size n, the traditional method needs n-1 combinations and n-1 layers; while the parallel method needs n-2 combinations and [log 2( n ? 1)] layers, in each layer the combinations are parallel, which can improve efficiency of the reconstruction.To avoid the effect of cumulate error in conventional iterative reconstruction algorithms, a linear rewound method is put forward. In each iterative step, after the reconstruction of the current view, use the information to re-estimate all the structures that can be seen then, and update the motion of the cameras. The rewound step will reduce the efficiency of the reconstruction process, since all the methods used in reconstruction are linear and the incremental computation of structure from motion process can be ignored compared to the combinations of correspondence process, it will affect the reconstruction very slightly.A nonlinear optimization algorithm, bundle adjustment, is used to optimize the initialize projective reconstruction result obtained by above methods. A Pollefeys's linear self-calibration method is adopted to upgrade the result from projective space to metric space, bundle adjustment is again used in metric space to achieve a final visible result of structures of the scene and motion of the cameras.