TL;DR
ResDepth is a neural network that learns a geometric prior to refine stereo digital surface models from high-resolution satellite images, improving accuracy and robustness across different urban environments.
Contribution
It introduces ResDepth, a deep residual prior that enhances stereo DSMs by incorporating learned geometric knowledge, adaptable to various imaging conditions and cities.
Findings
Consistently improves stereo DSM quality
Captures meaningful urban geometric features
Generalizes across cities and imaging conditions
Abstract
Modern optical satellite sensors enable high-resolution stereo reconstruction from space. But the challenging imaging conditions when observing the Earth from space push stereo matching to its limits. In practice, the resulting digital surface models (DSMs) are fairly noisy and often do not attain the accuracy needed for high-resolution applications such as 3D city modeling. Arguably, stereo correspondence based on low-level image similarity is insufficient and should be complemented with a-priori knowledge about the expected surface geometry beyond basic local smoothness. To that end, we introduce ResDepth, a convolutional neural network that learns such an expressive geometric prior from example data. ResDepth refines an initial, raw stereo DSM while conditioning the refinement on the images. I.e., it acts as a smart, learned post-processing filter and can seamlessly complement any…
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