# Leveraging Photogrammetric Mesh Models for Aerial-Ground Feature Point Matching Toward Integrated 3D Reconstruction

arXiv: https://arxiv.org/abs/2002.09085 · HTML: https://mlsanity.com/paper/2002.09085/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis](https://mlsanity.com/paper/2504.13157/) (2025)
- [3D reconstruction from spherical images: A review of techniques, applications, and prospects](https://mlsanity.com/paper/2302.04495/) (2023)
- [CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction](https://mlsanity.com/paper/2501.01695/) (2025)
- [Automated 3D-GS Registration and Fusion via Skeleton Alignment and Gaussian-Adaptive Features](https://mlsanity.com/paper/2507.20480/) (2025)
- [Aerial-Ground Image Feature Matching via 3D Gaussian Splatting-based Intermediate View Rendering](https://mlsanity.com/paper/2509.19898/) (2025)
- [Metadata-free Georegistration of Ground and Airborne Imagery](https://mlsanity.com/paper/2503.04927/) (2025)
- [Cross-modal feature fusion for robust point cloud registration with ambiguous geometry](https://mlsanity.com/paper/2505.13088/) (2025)
- [Semantic Image Translation for Repairing the Texture Defects of Building Models](https://mlsanity.com/paper/2303.17418/) (2023)
- [StructuredMesh: 3D Structured Optimization of Faccade Components on Photogrammetric Mesh Models using Binary Integer Programming](https://mlsanity.com/paper/2306.04184/) (2023)
- [Robust structure from motion for aerial-ground images via detector-free feature matching and multi-view track refinement](https://mlsanity.com/paper/2608.15251/) (2026)

## Abstract (arXiv)

Integration of aerial and ground images has been proved as an efficient approach to enhance the surface reconstruction in urban environments. However, as the first step, the feature point matching between aerial and ground images is remarkably difficult, due to the large differences in viewpoint and illumination conditions. Previous studies based on geometry-aware image rectification have alleviated this problem, but the performance and convenience of this strategy is limited by several flaws, e.g. quadratic image pairs, segregated extraction of descriptors and occlusions. To address these problems, we propose a novel approach: leveraging photogrammetric mesh models for aerial-ground image matching. The methods of this proposed approach have linear time complexity with regard to the number of images, can explicitly handle low overlap using multi-view images and can be directly injected into off-the-shelf structure-from-motion (SfM) and multi-view stereo (MVS) solutions. First, aerial and ground images are reconstructed separately and initially co-registered through weak georeferencing data. Second, aerial models are rendered to the initial ground views, in which the color, depth and normal images are obtained. Then, the synthesized color images and the corresponding ground images are matched by comparing the descriptors, filtered by local geometrical information, and then propagated to the aerial views using depth images and patch-based matching. Experimental evaluations using various datasets confirm the superior performance of the proposed methods in aerial-ground image matching. In addition, incorporation of the existing SfM and MVS solutions into these methods enables more complete and accurate models to be directly obtained.