Multi-view stereo infers the 3D geometry from a set of images captured from several known positions and viewpoints. It is one of the most important components of 3D reconstruction. Recently, deep learning has been increasingly used to solve several 3D vision problems due to the predominating performance, including the multi-view stereo problem. This paper presents a comprehensive review, covering recent deep learning methods for multi-view stereo. These methods are mainly categorized into depth map based and volumetric based methods according to the 3D representation form, and representative methods are reviewed in detail. Specifically, the plane sweep based methods leveraging depth maps are presented following the stage of approaches, i.e. feature extraction, cost volume construction, cost volume regularization, depth map regression and post-processing. This review also summarizes several widely used datasets and their corresponding metrics for evaluation. Finally, several insightful observations and challenges are put forward enlightening future research directions.

多视图立体从从多个已知位置和视点捕获的一组图像中推断出 3D 几何形状。它是 3D 重建最重要的组成部分之一。最近，由于主要性能，深度学习越来越多地用于解决几个 3D 视觉问题，包括多视图立体问题。本文进行了全面回顾，涵盖了多视图立体的最新深度学习方法。这些方法根据3D表示形式主要分为基于深度图的方法和基于体积的方法，并详细回顾了代表性方法。具体来说，利用深度图的基于平面扫描的方法是在方法阶段之后提出的，即特征提取、成本量构建、成本量正则化、深度图回归和后处理。本综述还总结了几个广泛使用的数据集及其相应的评估指标。最后，提出了一些有见地的观察和挑战，以启发未来的研究方向。