SeFM: A Sequential Feature Point Matching Algorithm for Object 3D Reconstruction
This addresses a key bottleneck in 3D reconstruction for applications like computer vision, though it is incremental as it builds on existing epipolar geometry and matching techniques.
The paper tackles the problem of insufficient feature point matching in 3D reconstruction from two images by proposing SeFM, a sequential algorithm that achieves around 1,000 to 10,000 times more matching dot-pairs than conventional methods and outperforms SIFT and SURF in precision and recall.
3D reconstruction is a fundamental issue in many applications and the feature point matching problem is a key step while reconstructing target objects. Conventional algorithms can only find a small number of feature points from two images which is quite insufficient for reconstruction. To overcome this problem, we propose SeFM a sequential feature point matching algorithm. We first utilize the epipolar geometry to find the epipole of each image. Rotating along the epipole, we generate a set of the epipolar lines and reserve those intersecting with the input image. Next, a rough matching phase, followed by a dense matching phase, is applied to find the matching dot-pairs using dynamic programming. Furthermore, we also remove wrong matching dot-pairs by calculating the validity. Experimental results illustrate that SeFM can achieve around 1,000 to 10,000 times matching dot-pairs, depending on individual image, compared to conventional algorithms and the object reconstruction with only two images is semantically visible. Moreover, it outperforms conventional algorithms, such as SIFT and SURF, regarding precision and recall.