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 this https URL

本文提出了一种新颖的跨尺度变换器来处理不同阶段的特征表示，同时引入了自适应匹配感知变换器、双特征引导聚合和特征度量损失等方法，以提高深度估计的准确性。实验证明，这种方法在 DTU 数据集和 Tanks and Temples 基准测试中取得了最先进的结果。