  Learning-based multi-view stereo (MVS) methods have made impressive progress
and surpassed traditional methods in recent years. However, their accuracy and
completeness are still struggling. In this paper, we propose a new method to
enhance the performance of existing networks inspired by contrastive learning
and feature matching. First, we propose a Contrast Matching Loss (CML), which
treats the correct matching points in depth-dimension as positive sample and
other points as negative samples, and computes the contrastive loss based on
the similarity of features. We further propose a Weighted Focal Loss (WFL) for
better classification capability, which weakens the contribution of
low-confidence pixels in unimportant areas to the loss according to predicted
confidence. Extensive experiments performed on DTU, Tanks and Temples and
BlendedMVS datasets show our method achieves state-of-the-art performance and
significant improvement over baseline network.
