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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >Robustifying Relative Orientations With Respect to Repetitive Structures and Very Short Baselines for Global SfM
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Robustifying Relative Orientations With Respect to Repetitive Structures and Very Short Baselines for Global SfM

机译：对全球SFM的重复结构和非常短的基线强制化相对取向

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Recently, global SfM has been attracting many researchers, mainly because of its time efficiency. Most of these methods are based on averaging relative orientations (ROs). Therefore, eliminating incorrect ROs is of great significance for improving the robustness of global SfM. In this paper, we propose a method to eliminate wrong ROs which have resulted from repetitive structure (RS) and very short baselines (VSB). We suggest two corresponding criteria that indicate the quality of ROs. These criteria are functions of potentially conjugate points resulting from local image matching of image pairs, followed by a geometry check using the 5-point algorithm combined with RANSAC. RS is detected based on counts of corresponding conjugate points of the various pairs, while VSB is found by inspecting the intersection angles of corresponding image rays. Based on these two criteria, incorrect ROs are eliminated. We demonstrate the proposed method on various datasets by inserting our refined ROs into a global SfM pipeline. The experiments show that compared to other methods we can generate the better results in this way.
机译：最近，全球SFM一直吸引了许多研究人员，主要是因为其时间效率。这些方法中的大多数基于平均相对取向（ROS）。因此，消除不正确的ROS对于改善全局SFM的稳健性具有重要意义。在本文中，我们提出了一种消除由重复结构（RS）和非常短的基线（VSB）产生的错误ROS的方法。我们建议两个表明ROS质量的相应标准。这些标准是由局部图像对产生的潜在共轭点的函数，然后使用与Ransac组合的5点算法进行几何检查。基于各种对的相应共轭点的计数来检测RS，而通过检查相应图像光线的交叉角来找到VSB。基于这两个标准，消除了不正确的ROS。我们通过将精制的ROS插入全球SFM管道来展示各个数据集上的提出方法。实验表明，与其他方法相比，我们可以以这种方式产生更好的结果。

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