  Learning-based multi-view stereo (MVS) method heavily relies on feature
matching, which requires distinctive and descriptive representations. An
effective solution is to apply non-local feature aggregation, e.g.,
Transformer. Albeit useful, these techniques introduce heavy computation
overheads for MVS. Each pixel densely attends to the whole image. In contrast,
we propose to constrain non-local feature augmentation within a pair of lines:
each point only attends the corresponding pair of epipolar lines. Our idea
takes inspiration from the classic epipolar geometry, which shows that one
point with different depth hypotheses will be projected to the epipolar line on
the other view. This constraint reduces the 2D search space into the epipolar
line in stereo matching. Similarly, this suggests that the matching of MVS is
to distinguish a series of points lying on the same line. Inspired by this
point-to-line search, we devise a line-to-point non-local augmentation
strategy. We first devise an optimized searching algorithm to split the 2D
feature maps into epipolar line pairs. Then, an Epipolar Transformer (ET)
performs non-local feature augmentation among epipolar line pairs. We
incorporate the ET into a learning-based MVS baseline, named ET-MVSNet.
ET-MVSNet achieves state-of-the-art reconstruction performance on both the DTU
and Tanks-and-Temples benchmark with high efficiency. Code is available at
https://github.com/TQTQliu/ET-MVSNet.
