  Recently, Transformers were shown to enhance the performance of multi-view
stereo by enabling long-range feature interaction. In this work, we propose
Window-based Transformers (WT) for local feature matching and global feature
aggregation in multi-view stereo. We introduce a Window-based Epipolar
Transformer (WET) which reduces matching redundancy by using epipolar
constraints. Since point-to-line matching is sensitive to erroneous camera pose
and calibration, we match windows near the epipolar lines. A second Shifted WT
is employed for aggregating global information within cost volume. We present a
novel Cost Transformer (CT) to replace 3D convolutions for cost volume
regularization. In order to better constrain the estimated depth maps from
multiple views, we further design a novel geometric consistency loss (Geo Loss)
which punishes unreliable areas where multi-view consistency is not satisfied.
Our WT multi-view stereo method (WT-MVSNet) achieves state-of-the-art
performance across multiple datasets and ranks $1^{st}$ on Tanks and Temples
benchmark.
