  Recent deep multi-view stereo (MVS) methods have widely incorporated
transformers into cascade network for high-resolution depth estimation,
achieving impressive results. However, existing transformer-based methods are
constrained by their computational costs, preventing their extension to finer
stages. In this paper, we propose a novel cross-scale transformer (CT) that
processes feature representations at different stages without additional
computation. Specifically, we introduce an adaptive matching-aware transformer
(AMT) that employs different interactive attention combinations at multiple
scales. This combined strategy enables our network to capture intra-image
context information and enhance inter-image feature relationships. Besides, we
present a dual-feature guided aggregation (DFGA) that embeds the coarse global
semantic information into the finer cost volume construction to further
strengthen global and local feature awareness. Meanwhile, we design a feature
metric loss (FM Loss) that evaluates the feature bias before and after
transformation to reduce the impact of feature mismatch on depth estimation.
Extensive experiments on DTU dataset and Tanks and Temples (T\&T) benchmark
demonstrate that our method achieves state-of-the-art results. Code is
available at https://github.com/wscstrive/CT-MVSNet.
