  As a fundamental problem in computer vision, multi-view stereo (MVS) aims at
recovering the 3D geometry of a target from a set of 2D images. Recent advances
in MVS have shown that it is important to perceive non-local structured
information for recovering geometry in low-textured areas. In this work, we
propose a Hierarchical Prior Mining for Non-local Multi-View Stereo (HPM-MVS).
The key characteristics are the following techniques that exploit non-local
information to assist MVS: 1) A Non-local Extensible Sampling Pattern (NESP),
which is able to adaptively change the size of sampled areas without becoming
snared in locally optimal solutions. 2) A new approach to leverage non-local
reliable points and construct a planar prior model based on K-Nearest Neighbor
(KNN), to obtain potential hypotheses for the regions where prior construction
is challenging. 3) A Hierarchical Prior Mining (HPM) framework, which is used
to mine extensive non-local prior information at different scales to assist 3D
model recovery, this strategy can achieve a considerable balance between the
reconstruction of details and low-textured areas. Experimental results on the
ETH3D and Tanks \& Temples have verified the superior performance and strong
generalization capability of our method. Our code will be released.
