论文标题
自我校准支持的强大投影结构从运动中
Self-Calibration Supported Robust Projective Structure-from-Motion
论文作者
论文摘要
典型的结构从动作（SFM）管道依赖于查找图像之间的对应关系，恢复了观察到的场景的投影结构，并使用摄像头自校准约束将其升级到公制框架。解决每个问题主要是独立于其他问题进行的。例如，相机自校准通常假定正确的匹配，并且已经获得了良好的投影重建。在本文中，我们提出了一种统一的SFM方法，其中匹配过程由自校准约束支持。我们使用的想法是，良好的匹配应产生有效的校准。在此过程中，我们利用多视图对应框架内的绝对二次投影方程的双图像，以便从一组推定的对应关系中获得可靠的匹配。匹配过程将点分类为嵌入式或离群值，这是使用深神经网络以无监督的方式学习的。加上理论上的推理，为什么需要进行自校准约束，我们显示了实验结果，通过利用这些约束来证明强大的多视图匹配和准确的摄像机校准。
Typical Structure-from-Motion (SfM) pipelines rely on finding correspondences across images, recovering the projective structure of the observed scene and upgrading it to a metric frame using camera self-calibration constraints. Solving each problem is mainly carried out independently from the others. For instance, camera self-calibration generally assumes correct matches and a good projective reconstruction have been obtained. In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints. We use the idea that good matches should yield a valid calibration. In this process, we make use of the Dual Image of Absolute Quadric projection equations within a multiview correspondence framework, in order to obtain robust matching from a set of putative correspondences. The matching process classifies points as inliers or outliers, which is learned in an unsupervised manner using a deep neural network. Together with theoretical reasoning why the self-calibration constraints are necessary, we show experimental results demonstrating robust multiview matching and accurate camera calibration by exploiting these constraints.
