# Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor

arXiv: https://arxiv.org/abs/2307.04520 · HTML: https://mlsanity.com/paper/2307.04520/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [SIFT Meets CNN: A Decade Survey of Instance Retrieval](https://mlsanity.com/paper/1608.01807/) (2016)
- [Graph-Based Parallel Large Scale Structure from Motion](https://mlsanity.com/paper/1912.10659/) (2019)
- [Parallel Structure from Motion for UAV Images via Weighted Connected Dominating Set](https://mlsanity.com/paper/2206.11499/) (2022)
- [Image Retrieval for Structure-from-Motion via Graph Convolutional Network](https://mlsanity.com/paper/2009.08049/) (2020)
- [Optimized Views Photogrammetry: Precision Analysis and A Large-scale Case Study in Qingdao](https://mlsanity.com/paper/2206.12216/) (2022)
- [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, 2 of 2)

- [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)

SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation.