Optimized Views Photogrammetry: Precision Analysis and a Large-Scale Case Study in Qingdao

Abstract

Unmanned aerial vehicle (UAVs) have become one of the widely used remote sensing platforms and played a critical role in the construction of smart cities. However, due to the complex environment in urban scenes, secure, and accurate data acquisition brings great challenges to 3-D modeling and scene updating. Optimal trajectory planning of UAVs and accurate data collection of onboard cameras are nontrivial issues in urban modeling. This study presents the principle of optimized views photogrammetry and verifies its precision and potential in large-scale 3-D modeling. Different from oblique photogrammetry, optimized views photogrammetry uses rough models to generate and optimize UAV trajectories, which is achieved through the consideration of model point reconstructability and view point redundancy. Based on the principle of optimized views photogrammetry, this study first conducts a precision analysis of 3-D models by using UAV images of optimized views photogrammetry and then executes a large-scale case study in the urban region of Qingdao City, China, to verify its engineering potential. By using GCPs for image orientation precision analysis and terrestrial laser scanning (TLS) point clouds for model quality analysis, experimental results show that optimized views photogrammetry could construct stable image connection networks and could achieve comparable image orientation accuracy. Benefiting from the accurate image acquisition strategy, the quality of mesh models significantly improves, especially for urban areas with serious occlusions, in which 3 to 5 times of higher accuracy has been achieved. Besides, the case study in Qingdao City verifies that optimized views photogrammetry can be a reliable and powerful solution for the large-scale 3-D modeling in complex urban scenes.

A 3D urban scene reconstruction enhancement approach based on adaptive viewpoint selection of panoramic videos

“…The experimental results demonstrate that the accuracies of different data fall within the ranges of 0.03–0.056, 0.049–0.09 and 0.08–0.18. These accuracies are comparable to those achieved by oblique photogrammetry and the latest optimised‐view photogrammetry (Li et al., 2023). The experimental data was collected separately from the oblique data, with different acquisition times and weather conditions.…”

“…Figure 23 presents a further visual interpretation of the modelling effects in near‐ground areas: “OP” represents models generated from oblique photography, “OV” represents models generated from optimised‐view photography based on the ETE strategy (Li et al., 2023) and “ER” refers to models generated by the method of this study. We selected two specific details in each scene.…”

“…They employed max-min optimisation and reconstructability heuristics to select a minimum set of viewpoints from among the candidates, ensuring robust reconstruction quality and efficiency. In a recent work by Li et al (2023), the ETE strategy was combined with photogrammetry to develop the concept of optimised-view photogrammetry. However, all the above methods require a definition of no-fly zones for UAVs to ensure safe flight in complex urban environments.…”

“…However, all the above methods require a definition of no-fly zones for UAVs to ensure safe flight in complex urban environments. For instance, the method proposed by Li et al (2023) set a minimum height of approximately 35 m above the ground based on tests conducted in real scenes. Under these circumstances, there can still be texture loss due to partial occlusions of near-ground façades, especially when the buildings have overhanging structures (as illustrated in Figure 1b).…”

“…In a recent work by Li et al. (2023), the ETE strategy was combined with photogrammetry to develop the concept of optimised‐view photogrammetry. However, all the above methods require a definition of no‐fly zones for UAVs to ensure safe flight in complex urban environments.…”