  This paper proposes a network, referred to as MVSTR, for Multi-View Stereo
(MVS). It is built upon Transformer and is capable of extracting dense features
with global context and 3D consistency, which are crucial to achieving reliable
matching for MVS. Specifically, to tackle the problem of the limited receptive
field of existing CNN-based MVS methods, a global-context Transformer module is
first proposed to explore intra-view global context. In addition, to further
enable dense features to be 3D-consistent, a 3D-geometry Transformer module is
built with a well-designed cross-view attention mechanism to facilitate
inter-view information interaction. Experimental results show that the proposed
MVSTR achieves the best overall performance on the DTU dataset and strong
generalization on the Tanks & Temples benchmark dataset.
