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Multi-view stereo (MVS) map based 3D range reconstruction is to generate 3D ranges by analyzing the surrounding snapshots from different perspectives. Different to the traditional method which employing the expensive and difficult maintaining laser range devices to calibrate the range of the real 3D objects, MVS has achieved its success by seeking the geometrical correlations between the correspondences from the snapshot of different perspectives. The concerning of MVS keeps rising thanks to the fast development of digital maps and 3D printing. Several algorithms with regard to MVS has been well developed and achieved their success with regard to reconstruction of 3D ranges. Meanwhile, most of the algorithms were mainly focusing on the fusion and merging of different scenes and surface refinement. Less capability of the feature matching algorithms on the affine invariant images renders the current MVS algorithms need huge amount of images with tiny perspective differences. In this paper, we will propose a new MVS algorithm, deploying our previous published Affine Invariant Feature Descriptor (AIFD) to detect and match the correspondences from different perspectives and applying Homograph matrix and segmentation to define the planes of the objects. Thanks to the AIFD and Homograph based projection model, our proposed MVS algorithm outperform other MVS algorithms in terms of speed and efficiency.

AIFD Based 2D Image Registration to Multi-View Stereo Mapped 3D Models