Experimental Technology and Management

[Objective] The construction of high-fidelity three-dimensional(3D) models for large-scale engineering projects remains a challenge because of the prolonged data acquisition cycles inherent to such tasks. These extended cycles often lead to overall visual dimness of the reconstructed models, as well as practical difficulties in coordinating heterogeneous resources across multiple stakeholders. Addressing these challenges requires integrating advanced data acquisition technologies with efficient modeling pipelines and user-oriented platforms for planning and analysis. [Methods] To this end, this study proposes a novel engineering planning platform that combines unmanned aerial vehicle(UAV) oblique photogrammetry, realistic 3D modeling, and modern web-based development frameworks into a unified technical workflow. The proposed platform aims to improve the perceptual quality of 3D reconstructions and to meet broader demands for resource coordination, visualization, and interactive planning in complex engineering scenarios. The 3D reconstruction component of the platform is based on widely adopted structure-from-motion(SfM) and multiview stereo(MVS) methods, which together form the foundation of contemporary photogrammetric pipelines. However, conventional implementations of SfM-MVS often suffer from illumination inconsistencies and texture degradation, especially when applied to extensive scenes collected over long time spans. To overcome these limitations, this study introduces a block-input redundancy strategy. Instead of using a single block point cloud from the SfM phase as the sole input to the MVS phase, the proposed method repeatedly feeds identical block data into the MVS reconstruction process. This design leverages the color-consistency fusion mechanism and voxel-level color aggregation algorithms, thereby suppressing local illumination discrepancies and enhancing global texture fidelity. Therefore, the reconstructed models exhibit improved visual brightness, greater detail preservation, and more consistent rendering across large spatial extents. [Results] To evaluate the practicality and effectiveness of the proposed approach, the methodology was applied to the Pinglu Canal Project, a representative large-scale infrastructure undertaking in China. The experimental results showed that the redundant block-input method significantly enhances the visual realism and coherence of 3D models, particularly in brightness and texture clarity. In parallel, the web-based platform supports data analysis, multisource information integration, and scenario-driven planning. Its interactive visualization functions enable engineers, planners, and decision-makers to collaboratively explore 3D environments, allocate resources, and simulate planning outcomes with enhanced efficiency and transparency. Such capabilities are essential for advancing the digital transformation of engineering practices, where the integration of accurate spatial data with intuitive decision-support systems is becoming increasingly indispensable. [Conclusions] Overall, the findings of this study highlight both the methodological innovation and the practical significance of the proposed platform. The combination of UAV-based photogrammetry, enhanced SfM-MVS reconstruction, and web-enabled planning tools is a robust and adaptable solution for large-scale engineering applications. Furthermore, the scalability and generalizability of the block-input redundancy method imply that it can be applied to other large-scale projects requiring high-quality 3D reconstructions under variable acquisition conditions. By incorporating advanced photogrammetric algorithms with engineering planning, this research contributes to the ongoing informatization of infrastructure projects and offers a feasible technological pathway for integrating 3D modeling with intelligent decision support.