The primary contribution of this paper is an efficient Structure from Motion (SfM) solution for oblique unmanned aerial vehicle (UAV) images. First, an algorithm, considering spatial relationship constrains between image footprints, is designed for match pair selection with assistant of UAV flight control data and oblique camera mounting angles. Second, a topological connection network (TCN), represented by an undirected weighted graph, is constructed from initial match pairs, which encodes overlap area and intersection angle into edge weights. Then, an algorithm, termed MST-Expansion, is proposed to extract the match graph from the TCN where the TCN is firstly simplified by a maximum spanning tree (MST). By further analysis of local structure in the MST, expansion operations are performed on the nodes of the MST for match graph enhancement, which is achieved by introducing critical connections in two expansion directions. Finally, guided by the match graph, an efficient SfM solution is proposed, and its validation is verified through comprehensive analysis and comparison using three UAV datasets captured with different oblique multi-camera systems. Experiment results demonstrate that the efficiency of image matching is improved with a speedup ratio ranging from 19 to 35, and competitive orientation accuracy is achieved from both relative bundle adjustment (BA) without GCPs (Ground Control Points) and absolute BA with GCPs. At the same time, images in the three datasets are successfully oriented. For orientation of oblique UAV images, the proposed method can be a more efficient solution.
Efficient On-Board Processing of Oblique UAV Video for Rapid Flood Extent Mapping
IDLab, Department of Information Technology, Ghent University - imec（IDLab，信息科技系，根特大学 - imec）；Environmental Intelligence Unit, VITO (Flemish Institute for Technological Research)（环境智能单元，VITO（佛兰德斯技术研究院））
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs（农业农村部光谱传感重点实验室）；College of Agriculture and Biotechnology, Zhejiang University（浙江大学农业与生物技术学院）
USU-Corn-WeedDB: A UAV RGB Image Dataset for Multi-Species Weed Detection in Forage Corn
Department of Applied Sciences, Technology, and Education, Utah State University（应用科学、技术和教育系，犹他州立大学）；Department of Plants, Soils & Climate, Utah State University（植物、土壤与气候系，犹他州立大学）；Department of Civil and Environmental Engineering, Utah State University（土木与环境工程系，犹他州立大学）
Towards Gold-Standard Depth Estimation for Tree Branches in UAV Forestry: Benchmarking Deep Stereo Matching Methods
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
