An Effective Multiview Stereo Method for Uncalibrated Images
For most dense multi-view stereo methods, the process of finding correspondences is the basis and is independent of acquiring 3D information, and this often brings about erroneous correspondences followed by erroneous 3D information. To tackle this problem, by expanding matched points and by expanding 3D patches, this paper proposes an effective approach to acquire dense and accurate point clouds from multi-view uncalibrated images. In the approach, two novel algorithms are newly designed and are placed before and after the Bundler: 1) the match expansion algorithm, which generates evenly distributed correspondences with geometric consistency; after using Bundler to produce geometry estimation and quasi-dense point clouds which are not dense and accurate, 2) the point-cloud expansion algorithm, which is proposed to improve the density and accuracy of point clouds by optimizing the geometry of each 3D patch and expanding each good patch to its neighborhood. A large number of experimental results demonstrate the proposed approach get more accurate and denser point clouds than the state-of-the-art methods. A quantitative evaluation shows the accuracy of the proposed method favorable to PMVS.
KeywordsMultiview stereo Match expansion Point-cloudexpansion
Unable to display preview. Download preview PDF.
- 1.Bosch, A., Zisserman, A., Muoz, X.: Image classification using random forests and ferns. In: IEEE 11th International Conference on Computer Vision (ICCV 2007), pp. 1–8 (2007)Google Scholar
- 5.Goesele, M., Snavely, N., Curless, B., Hoppe, H., Seitz, S.M.: Multi-view stereo for community photo collections. In: IEEE 11th International Conference on Computer Vision (ICCV 2007), pp. 1–8 (2007)Google Scholar
- 10.Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 519–528. IEEE (2006)Google Scholar
- 15.Strecha, C., von Hansen, W., Van Gool, L., Fua, P., Thoennessen, U.: On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1–8, June 2008Google Scholar
- 17.Uh, Y., Matsushita, Y., Byun, H.: Efficient multiview stereo by random-search and propagation. In: 2014 2nd International Conference on 3D Vision (3DV), vol. 1, pp. 393–400, December 2014Google Scholar
- 18.Vogiatzis, G., Torr, P.H.S., Cipolla, R.: Multi-view stereo via volumetric graph-cuts. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 391–398, June 2005Google Scholar