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In the domain of point cloud registration, the coarse-to-fine feature matching paradigm has received significant attention due to its impressive performance. This paradigm involves a two-step process: first, the extraction of multilevel features, and subsequently, the propagation of correspondences from coarse to fine levels. However, this approach faces two notable limitations. Firstly, the use of the Dual Softmax operation may promote one-to-one correspondences between superpoints, inadvertently excluding valuable correspondences. Secondly, it is crucial to closely examine the overlapping areas between point clouds, as only correspondences within these regions decisively determine the actual transformation. Considering these issues, we propose OAAFormer to enhance correspondence quality. On the one hand, we introduce a soft matching mechanism to facilitate the propagation of potentially valuable correspondences from coarse to fine levels. On the other hand, we integrate an overlapping region detection module to minimize mismatches to the greatest extent possible. Furthermore, we introduce a region-wise attention module with linear complexity during the fine-level matching phase, designed to enhance the discriminative capabilities of the extracted features. Tests on the challenging 3DLoMatch benchmark demonstrate that our approach leads to a substantial increase of about 7% in the inlier ratio, as well as an enhancement of 2%–4% in registration recall. Finally, to accelerate the prediction process, we replace the Conventional Random Sample Consensus (RANSAC) algorithm with the selection of a limited yet representative set of high-confidence correspondences, resulting in a 100 times speedup while still maintaining comparable registration performance. [ABSTRACT FROM AUTHOR]

With the increasing popularity of Internet of things (IoT) applications, mobile edge computing (MEC) is emerging as a new technology. Caching popular content to edge servers can effectively reduce backhaul time and satisfy a large number of users' access requests for the content. However, edge caching faces the problems of limited storage capacity in the edge server and limited service scope in MEC. Therefore, edge caching should consider allocating the limited caching resources reasonably to different content providers (CP) to obtain the high caching utility. In this paper, we study caching in a multi-CP scenario with multiple users. In the process of defining content popularity, we consider that CPs′ overall popularity degree satisfies the law of diminishing marginal effect, and define the user satisfaction function as the benefit of caching. Then, aiming at maximizing caching utility, we establish a game model of caching resource competition among CPs based on noncooperative game, and prove the existence of Nash equilibrium (NE). In addition, the Best Response based Multi-CP Caching (BRMC) algorithm is proposed to obtain the best caching strategy. Finally, iterative experiments validate the convergence of the BRMC, and comparative experiments show that the BRMC can achieve a high caching utility. [ABSTRACT FROM AUTHOR]