Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

We present a method to make an accurate and dense reconstruction from the input of video captured by a free moving handheld camera in real time. By the method firstly, the positions of the camera and sparse 3D points are estimated by simultaneous localization mapping. Then the depth maps of selected reference frames are computed from corresponding camera bundles. Lastly a novel linear algorithm is also proposed to integrate all the depth maps into dense meshes partially. The main contributions of this paper are in the following points: the reference frames and corresponding camera bundles are able to be selected automatically, then accurate and smooth depth maps are generated in real time, and the depth maps are merged into a dense mesh by using a linear algorithm based on the error clouds optimization. Our algorithm is implemented on dual CPU and graphics processing unit in a parallel framework for improving the performance. Copyright © 2013 John Wiley & Sons, Ltd.

No abstract

No abstract