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

TL;DR

This paper introduces an efficient method for match pair retrieval in large-scale UAV image processing, utilizing graph-indexed global descriptors to significantly speed up SfM reconstruction while maintaining accuracy.

Contribution

It proposes a novel approach combining online codebook training, VLAD aggregation, and HNSW graph indexing for fast match pair retrieval in UAV image SfM workflows.

Findings

Achieves 36 to 108 times faster match pair retrieval.

Maintains competitive accuracy in SfM reconstruction.

Validates effectiveness on three large-scale UAV datasets.

Abstract

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.