arXiv:2510.08839v1 Announce Type: cross
Abstract: Real-time multi-view 3D reconstruction is a mission-critical application for key edge-native use cases, such as fire rescue, where timely and accurate 3D scene modeling enables situational awareness and informed decision-making. However, the dynamic and unpredictable nature of edge resource availability introduces disruptions, such as degraded image quality, unstable network links, and fluctuating server loads, which challenge the reliability of the reconstruction pipeline. In this work, we present a reinforcement learning (RL)-based edge resource management framework for reliable 3D reconstruction to ensure high quality reconstruction within a reasonable amount of time, despite the system operating under a resource-constrained and disruption-prone environment. In particular, the framework adopts two cooperative Q-learning agents, one for camera selection and one for server selection, both of which operate entirely online, learning policies through interactions with the edge environment. To support learning under realistic constraints and evaluate system performance, we implement a distributed testbed comprising lab-hosted end devices and FABRIC infrastructure-hosted edge servers to emulate smart city edge infrastructure under realistic disruption scenarios. Results show that the proposed framework improves application reliability by effectively balancing end-to-end latency and reconstruction quality in dynamic environments.
Expert Commentary: Real-time Multi-View 3D Reconstruction with Reinforcement Learning
The concept of real-time multi-view 3D reconstruction using reinforcement learning is a groundbreaking development in the field of multimedia information systems. This innovative approach addresses the challenges posed by dynamic and unpredictable edge environments, where traditional reconstruction pipelines may struggle to maintain reliability and accuracy.
By utilizing reinforcement learning agents for camera and server selection, this framework leverages AI-driven decision-making to optimize resource utilization and adapt to changing conditions in real-time. This multi-disciplinary approach combines computer vision, machine learning, and edge computing to enhance the performance of mission-critical applications such as fire rescue operations.
Furthermore, the use of a distributed testbed featuring lab-hosted end devices and FABRIC infrastructure-hosted edge servers adds a layer of realism to the evaluation process. This setup allows researchers to simulate a smart city edge infrastructure and test the framework’s effectiveness under realistic disruption scenarios, providing valuable insights into its potential real-world applications.
Overall, this research not only advances the field of real-time 3D reconstruction but also contributes to the broader fields of artificial reality, augmented reality, and virtual reality by demonstrating the potential for AI-driven optimization in dynamic and resource-constrained environments. As technology continues to evolve, we can expect to see further innovations in multimedia information systems that improve the efficiency and reliability of complex tasks across various domains.