# Optimized Views Photogrammetry: Precision Analysis and A Large-scale Case Study in Qingdao

arXiv: https://arxiv.org/abs/2206.12216 · HTML: https://mlsanity.com/paper/2206.12216/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Object-Aware Guidance for Autonomous Scene Reconstruction](https://mlsanity.com/paper/1805.07794/) (2018)
- [Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction](https://mlsanity.com/paper/1705.09314/) (2017)
- [Efficient Structure from Motion for Oblique UAV Images Based on Maximal Spanning Tree Expansions](https://mlsanity.com/paper/1705.03212/) (2017)

## Built on by (most cited first, 8 of 8)

- [3D reconstruction from spherical images: A review of techniques, applications, and prospects](https://mlsanity.com/paper/2302.04495/) (2023)
- [3D Reconstruction of Spherical Images based on Incremental Structure from Motion](https://mlsanity.com/paper/2306.12770/) (2023)
- [Aerial-Ground Image Feature Matching via 3D Gaussian Splatting-based Intermediate View Rendering](https://mlsanity.com/paper/2509.19898/) (2025)
- [Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor](https://mlsanity.com/paper/2307.04520/) (2023)
- [On-the-fly Feedback SfM: Online Explore-and-Exploit UAV Photogrammetry with Incremental Mesh Quality-Aware Indicator and Predictive Path Planning](https://mlsanity.com/paper/2512.02375/) (2025)
- [Semantic Image Translation for Repairing the Texture Defects of Building Models](https://mlsanity.com/paper/2303.17418/) (2023)
- [Efficient feature matching for UAV images based on compact GPU data scheduling](https://mlsanity.com/paper/2505.22089/) (2025)
- [UAVPairs: A Challenging Benchmark for Match Pair Retrieval of Large-scale UAV Images](https://mlsanity.com/paper/2505.22098/) (2025)

## Abstract (arXiv)

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 3D modeling and scene updating. Optimal trajectory planning of UAVs and accurate data collection of onboard cameras are non-trivial issues in urban modeling. This study presents the principle of optimized views photogrammetry and verifies its precision and potential in large-scale 3D 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 3D 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 TLS (terrestrial laser scanning) 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 3D modeling in complex urban scenes.