论文标题
全球SFM有效的初始姿势盖产生
Efficient Initial Pose-graph Generation for Global SfM
论文作者
论文摘要
我们提出了加快全局结构算法的初始姿势绘制生成的方法。为了避免通过Flann形成暂定点对应关系和RANSAC的几何验证，这是姿势创建中最耗时的步骤，我们提出了两种新方法 - 构建了图像对通常连续匹配的事实。因此，可以从部分建造的姿势图中的路径中回收候选相对姿势。考虑到图像的全球相似性和姿势形式边缘的质量，我们为A*遍历提出了一种启发式。鉴于从路径的相对姿势，基于描述符的特征匹配是通过利用已知的表现几何形状来“轻巧”的。为了加快应用RANSAC时基于ProSAC的采样，我们提出了一种第三种方法，以通过以前的估计来订购其对应关系。从1DSFM数据集对402130图像对测试了该算法，它们加快了功能匹配的17次，并估算了5次。
We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms. To avoid forming tentative point correspondences by FLANN and geometric verification by RANSAC, which are the most time-consuming steps of the pose-graph creation, we propose two new methods - built on the fact that image pairs usually are matched consecutively. Thus, candidate relative poses can be recovered from paths in the partly-built pose-graph. We propose a heuristic for the A* traversal, considering global similarity of images and the quality of the pose-graph edges. Given a relative pose from a path, descriptor-based feature matching is made "light-weight" by exploiting the known epipolar geometry. To speed up PROSAC-based sampling when RANSAC is applied, we propose a third method to order the correspondences by their inlier probabilities from previous estimations. The algorithms are tested on 402130 image pairs from the 1DSfM dataset and they speed up the feature matching 17 times and pose estimation 5 times.
