  Learning-based Multi-View Stereo (MVS) methods warp source images into the
reference camera frustum to form 3D volumes, which are fused as a cost volume
to be regularized by subsequent networks. The fusing step plays a vital role in
bridging 2D semantics and 3D spatial associations. However, previous methods
utilize extra networks to learn 2D information as fusing cues, underusing 3D
spatial correlations and bringing additional computation costs. Therefore, we
present MVSTER, which leverages the proposed epipolar Transformer to learn both
2D semantics and 3D spatial associations efficiently. Specifically, the
epipolar Transformer utilizes a detachable monocular depth estimator to enhance
2D semantics and uses cross-attention to construct data-dependent 3D
associations along epipolar line. Additionally, MVSTER is built in a cascade
structure, where entropy-regularized optimal transport is leveraged to
propagate finer depth estimations in each stage. Extensive experiments show
MVSTER achieves state-of-the-art reconstruction performance with significantly
higher efficiency: Compared with MVSNet and CasMVSNet, our MVSTER achieves 34%
and 14% relative improvements on the DTU benchmark, with 80% and 51% relative
reductions in running time. MVSTER also ranks first on Tanks&Temples-Advanced
among all published works. Code is released at https://github.com/JeffWang987.
