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Found 6 papers, 4 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.
SparseSat-NeRF: Dense Depth Supervised Neural Radiance Fields for Sparse Satellite Images
However, NeRF and its variants require many views to produce convincing scene's geometries which in earth observation satellite imaging is rare.
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.
A pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery
The Corona KH-4 reconnaissance satellite missions from 1962-1972 acquired panoramic stereo imagery with high spatial resolution of 1. 8-7. 5 m. The potential of 800, 000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery.
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.
