  Image-based 3D reconstruction is one of the most important tasks in Computer
Vision with many solutions proposed over the last few decades. The objective is
to extract metric information i.e. the geometry of scene objects directly from
images. These can then be used in a wide range of applications such as film,
games, virtual reality, etc. Recently, deep learning techniques have been
proposed to tackle this problem. They rely on training on vast amounts of data
to learn to associate features between images through deep convolutional neural
networks and have been shown to outperform traditional procedural techniques.
In this paper, we improve on the state-of-the-art two-view
structure-from-motion(SfM) approach of [11] by incorporating 4D correlation
volume for more accurate feature matching and reconstruction. Furthermore, we
extend it to the general multi-view case and evaluate it on the complex
benchmark dataset DTU [4]. Quantitative evaluations and comparisons with
state-of-the-art multi-view 3D reconstruction methods demonstrate its
superiority in terms of the accuracy of reconstructions.
