  Multi-view stereo (MVS) is a crucial task for precise 3D reconstruction. Most
recent studies tried to improve the performance of matching cost volume in MVS
by designing aggregated 3D cost volumes and their regularization. This paper
focuses on learning a robust feature extraction network to enhance the
performance of matching costs without heavy computation in the other steps. In
particular, we present a dynamic scale feature extraction network, namely,
CDSFNet. It is composed of multiple novel convolution layers, each of which can
select a proper patch scale for each pixel guided by the normal curvature of
the image surface. As a result, CDFSNet can estimate the optimal patch scales
to learn discriminative features for accurate matching computation between
reference and source images. By combining the robust extracted features with an
appropriate cost formulation strategy, our resulting MVS architecture can
estimate depth maps more precisely. Extensive experiments showed that the
proposed method outperforms other state-of-the-art methods on complex outdoor
scenes. It significantly improves the completeness of reconstructed models. As
a result, the method can process higher resolution inputs within faster
run-time and lower memory than other MVS methods. Our source code is available
at url{https://github.com/TruongKhang/cds-mvsnet}.
