论文标题
基本矩阵估计的端到端网络体系结构
An End to End Network Architecture for Fundamental Matrix Estimation
论文作者
论文摘要
在本文中，我们提出了一种新颖的端到端网络体系结构，直接从立体声图像中估算基本矩阵。为了建立完整的工作管道，负责在图像中查找对应关系的不同深层神经网络，执行异常值拒绝和计算基本矩阵，已集成到端到端网络体系结构中。 为了很好地训练网络并保留基本矩阵的几何特性，引入了新的损失函数。为了更合理地评估估计基本矩阵的准确性，我们设计了一个新的评估度量标准，该指标与可视化结果高度一致。对室外和室内数据集进行的实验表明，该网络的表现优于传统方法，以及先前基于深度学习的方法，对各种指标和实现了重大的性能提高。
In this paper, we present a novel end-to-end network architecture to estimate fundamental matrix directly from stereo images. To establish a complete working pipeline, different deep neural networks in charge of finding correspondences in images, performing outlier rejection and calculating fundamental matrix, are integrated into an end-to-end network architecture. To well train the network and preserve geometry properties of fundamental matrix, a new loss function is introduced. To evaluate the accuracy of estimated fundamental matrix more reasonably, we design a new evaluation metric which is highly consistent with visualization result. Experiments conducted on both outdoor and indoor data-sets show that this network outperforms traditional methods as well as previous deep learning based methods on various metrics and achieves significant performance improvements.
