Drones serve as the primary camera-mounted platform to collect high-resolution images of buildings from various viewpoints for 3D neural radiance fields (NeRF) reconstruction. However, the flexibility of drones in lowering flight heights to supplement richer building details and thus improve the reconstruction quality of NeRF has not been explored. Given the limited power of a drone and the rapidly changing nature of outdoor scenes, it is necessary to quickly predict an optimal supplementary lower flight height after the drone finishes capturing at a default height. The main challenges involve the implicit relationship between supplementary flight heights and reconstruction quality, the complexity of prediction task with little prior information, and the tension between the need for real-time decision making and the resource constraints inherent to on-drone task execution. In this work, we develop an end-to-end system pipeline with an offline-online decoupling feature. We first design a model architecture that embeds supplementary flight heights to predict the reconstruction quality of NeRF using images captured at the default height. To enhance model generalization ability in a cost-effective manner, we offline train an ensemble of multiple models over the samples constructed from sandbox buildings using building-level cross validation. During the online serving phase on the drone, the most appropriate model is selected based on feature matching between real and sandbox buildings to determine the optimal supplementary flight height. We build a testbed and evaluate on 16 sandbox buildings and 8 real-world buildings. Evaluation results demonstrate that our pipeline accurately identifies the optimal supplementary lower height in a few minutes and improves reconstruction quality.

We present HRM2Avatar, a novel framework for creating high-fidelity avatars from monocular phone scans, which can be rendered and animated in real-time on mobile devices. Monocular capture with commodity smartphones provides a low-cost, pervasive alternative to studio-grade multi-camera rigs, making avatar digitization accessible to non-expert users. Reconstructing high-fidelity avatars from single-view video sequences poses significant challenges due to deficient visual and geometric data relative to multi-camera setups. To address these limitations, at the data level, our method leverages two types of data captured with smartphones: static pose sequences for detailed texture reconstruction and dynamic motion sequences for learning pose-dependent deformations and lighting changes. At the representation level, we employ a lightweight yet expressive representation to reconstruct high-fidelity digital humans from sparse monocular data. First, we extract explicit garment meshes from monocular data to model clothing deformations more effectively. Second, we attach illumination-aware Gaussians to the mesh surface, enabling high-fidelity rendering and capturing pose-dependent lighting changes. This representation efficiently learns high-resolution and dynamic information from our tailored monocular data, enabling the creation of detailed avatars. At the rendering level, real-time performance is critical for rendering and animating high-fidelity avatars in AR/VR, social gaming, and on-device creation, demanding sub-frame responsiveness. Our fully GPU-driven rendering pipeline delivers 120 FPS on mobile devices and 90 FPS on standalone VR devices at 2K resolution, over 2.7 × faster than representative mobile-engine baselines. Experiments show that HRM2Avatar delivers superior visual realism and real-time interactivity at high resolutions, outperforming state-of-the-art monocular methods.

With the rapid development of mobile network, the use of mobile phones has become popular. People use mobile phones every day to surf the Internet, shop, socialize, work, etc. In the process of using mobile web services, users may be dissatisfied with the service perception, such as voice connectivity, Internet access, Slow Internet access and other common problems. If the customer is not satisfied with the communication service, the customer can usually complain about the quality of the communication service, so the frequency of the customer complaint has become an important evaluation index for the management of the operator. The quantity and frequency of customers ‘complaints about telecommunication service are increasing gradually, which brings challenges to the service quality and efficiency of telecommunication operators. This paper presents a methodology for customer complaints. The analysis system is based on the data of Horizontal pull- through, combined with big data analysis model, focus on the user’s response to the Internet slow, Internet access, voice access issues such as real-time positioning analysis, to provide customers with the first time solutions.