  In this paper, we present TransMVSNet, based on our exploration of feature
matching in multi-view stereo (MVS). We analogize MVS back to its nature of a
feature matching task and therefore propose a powerful Feature Matching
Transformer (FMT) to leverage intra- (self-) and inter- (cross-) attention to
aggregate long-range context information within and across images. To
facilitate a better adaptation of the FMT, we leverage an Adaptive Receptive
Field (ARF) module to ensure a smooth transit in scopes of features and bridge
different stages with a feature pathway to pass transformed features and
gradients across different scales. In addition, we apply pair-wise feature
correlation to measure similarity between features, and adopt
ambiguity-reducing focal loss to strengthen the supervision. To the best of our
knowledge, TransMVSNet is the first attempt to leverage Transformer into the
task of MVS. As a result, our method achieves state-of-the-art performance on
DTU dataset, Tanks and Temples benchmark, and BlendedMVS dataset. The code of
our method will be made available at https://github.com/MegviiRobot/TransMVSNet .
