As a fundamental problem in computer vision, multi-view stereo (MVS) aims at recovering the 3D geometry of the target from a set of 2D images. However, the reconstructed quality is significantly impacted by the presence of low-textured areas. In this paper, we propose a Hierarchical Prior Mining (HPM) framework for non-local multi-view stereo. Different from most existing works dedicated to focusing on local information and only using a single prior, HPM captures non-local structural cues and leverages multi-source priors for geometry recovery. Based on the framework, we first propose HPM-MVS, which obtains precise initial hypotheses through non-local operations, simultaneously constructing a better planar prior model in an HPM framework to further facilitate hypothesis generation. In addition, we futher propose HPM-MVS++, which excavates the structured region information of images and spatial geometric relationships of hypotheses as prior knowledge. Then, it incorporates them into probabilistic graphical models, ultimately deducing two novel multi-view matching costs. This significantly enhances the robustness to challenging situations and improves the completeness of the reconstruction. Experimental results on the ETH3D and Tanks & Temples have verified the superior performance and strong generalization capability of our approach.

Consistent correspondences between point clouds are vital to 3D vision tasks such as registration and recognition. In this paper, we present a mutual voting method for ranking 3D correspondences. The key insight is to achieve reliable scoring results for correspondences by refining both voters and candidates in a mutual voting scheme. First, a graph is constructed for the initial correspondence set with the pairwise compatibility constraint. Second, nodal clustering coefficients are introduced to preliminarily remove a portion of outliers and speed up the following voting process. Third, we model nodes and edges in the graph as candidates and voters, respectively. Mutual voting is then performed in the graph to score correspondences. Finally, the correspondences are ranked based on the voting scores and top-ranked ones are identified as inliers. Feature matching, 3D point cloud registration, and 3D object recognition experiments on various datasets with different nuisances and modalities verify that MV is robust to heavy outliers under different challenging settings, and can significantly boost 3D point cloud registration and 3D object recognition performance.

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 available at https://github.com/CLinvx/HPM-MVS.