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Found 4 papers, 3 papers with code
An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
To address this challenge, we propose a method for generating ground-truth disparity maps directly from Light Detection and Ranging (LiDAR) and images to produce a large and diverse dataset for six aerial datasets across four different areas and two areas with different resolution images.
DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching
Our features are learnt on large image tiles to be expressive and capture the scene's wider context.
Pointless Global Bundle Adjustment With Relative Motions Hessians
Bundle adjustment (BA) is the standard way to optimise camera poses and to produce sparse representations of a scene.
Feature matching for multi-epoch historical aerial images
The method consists of: (1) an inter-epoch DSMs matching to roughly co-register the orientations and DSMs (i. e, the 3D Helmert transformation), followed by (2) a precise inter-epoch feature matching using the original RGB images.
