论文标题
A-TVSNET：用于多视图立体深度估计的汇总两视立体声网络
A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth Estimation
论文作者
论文摘要
我们提出了一个基于学习的网络，以从多视图立体声（MVS）图像中进行深度图估计。我们提出的网络由三个子网络组成：1）一个基本网络，用于从非结构化立体图像对估算初始深度图估算，2）一个新型的改进网络，可利用光度和几何信息以及3）一个注意力多视图聚合框架，该框架可以在不同的立体图像之间进行有效的信息交换和集成。在各种MVS数据集上评估了所提出的网络，称为A-TVSNET，并显示出产生优于竞争方法的高质量深度图的能力。我们的代码可在https://github.com/daiszh/a-tvsnet上找到。
We propose a learning-based network for depth map estimation from multi-view stereo (MVS) images. Our proposed network consists of three sub-networks: 1) a base network for initial depth map estimation from an unstructured stereo image pair, 2) a novel refinement network that leverages both photometric and geometric information, and 3) an attentional multi-view aggregation framework that enables efficient information exchange and integration among different stereo image pairs. The proposed network, called A-TVSNet, is evaluated on various MVS datasets and shows the ability to produce high quality depth map that outperforms competing approaches. Our code is available at https://github.com/daiszh/A-TVSNet.
