Multi-view stereo in the Deep Learning Era: A comprehensive review

Abstract

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.