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AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data

Jason R. Laura·Kelvin Rodriguez·Adam Paquette·Evin Dunn
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Abstract

In this work we describe the AutoCNet library, written in Python, to support the application of Computer Vision techniques for n-image correspondence identication in remotely sensed planetary images and subsequent bundle adjustment. The library is designed to support exploratory data analysis, algorithm and processing pipeline development, and application at scale in High Performance Computing (HPC) environments for processing large data sets and generating foundational data products. We also present a brief case study illustrating high level usage for the Apollo 15 Metric camera.

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Jason R. Laura, Kelvin Rodriguez, Adam Paquette, Evin Dunn. 2018. AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data. https://doi.org/10.1016/j.softx.2018.02.001

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