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3D reconstruction is a fundamental issue in many applications and the featurepoint matching problem is a key step while reconstructing target objects.Conventional algorithms can only find a small number of feature points from twoimages which is quite insufficient for reconstruction. To overcome thisproblem, we propose SeFM a sequential feature point matching algorithm. Wefirst utilize the epipolar geometry to find the epipole of each image. Rotatingalong the epipole, we generate a set of the epipolar lines and reserve thoseintersecting with the input image. Next, a rough matching phase, followed by adense matching phase, is applied to find the matching dot-pairs using dynamicprogramming. Furthermore, we also remove wrong matching dot-pairs bycalculating the validity. Experimental results illustrate that SeFM can achievearound 1,000 to 10,000 times matching dot-pairs, depending on individual image,compared to conventional algorithms and the object reconstruction with only twoimages is semantically visible. Moreover, it outperforms conventionalalgorithms, such as SIFT and SURF, regarding precision and recall.

SeFM: A Sequential Feature Point Matching Algorithm for Object 3D Reconstruction