Deep learning-based multi-view stereo (MVS) methods enable dense point cloud reconstruction in texture-rich areas. However, existing methods incur significant computational costs to capture pixel dependencies for complete reconstruction in low-texture regions. Additionally, discrete depth layers in occluded environments hinder the cost volume’s ability to model object information effectively. To address these issues, we propose a spatial-aware multi-view stereo network with attention cost volume, termed SA-MVSNet. The network introduces the pixel-driven spatial interaction (PDSI) module, which integrates the hierarchical spatial location enhancement mechanism (HSLE) and the spatial context aggregation mechanism (SCA). Leveraging an efficient parallel architecture, the PDSI module captures pixel-level spatial dependencies with the HSLE and strengthens global contextual information through the SCA. This design improves the network’s ability to represent features in low-texture regions while maintaining high inference efficiency. Furthermore, SA-MVSNet incorporates an attention weight generation branch that refines the cost volume by aggregating multi-scale depth cues, effectively mitigating the impact of occlusion. Experiments on the DTU dataset and the Tanks and Temples dataset show that our method outperforms other learning-based methods, achieving superior performance and strong generalization ability.

With the commercialization of fifth-generation (5G) mobile communication technology and the rapid proliferation of mobile devices (MDs), demand for data computation is surging. This growth increases the reliance of MDs on low latency and high throughput. For this purpose, Mobile Edge Computing (MEC) enhances the user's data processing capability by offloading computation tasks to servers at the network edge. However, achieving high efficiency in task offloading is challenging due to factors such as decision complexity, network dynamics, and user data privacy protection. Additionally, energy causal constraints and the coupling between offloading proportions and resource distribution cannot be ignored. In this paper, we first establish a dynamic task offloading problem to optimize the long-term throughput of the system. Using perturbed Lyapunov optimization, we transform MD delay and energy threshold constraints into the stability control of corresponding virtual queues. Then, we propose the Lyapunov-guided federated deep reinforcement learning (DRL) online task offloading algorithm called LyFOTO, which combines a federated learning (FL) framework and an Actor-Critic (AC) model. Under favorable communication conditions, the LyFOTO algorithm adaptively boosts system throughput; under poorer conditions, it properly delays task offloading, without violating queue backlog constraints. Through mathematical analysis, we discuss the performance of the LyFOTO algorithm. Simulation experiments validate that LyFOTO effectively balances system throughput and device battery energy. Finally, Comparative results show that LyFOTO outperforms other benchmark algorithms in maximizing system throughput while ensuring task backlog and energy threshold constraints.

The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, aVehicle-assistedCollaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously.