Paper
Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments
Authors
Pankaj Deoli, Karthik Ranganath, Karsten Berns
Abstract
RGB-NIR image registration plays an important role in sensor-fusion, image enhancement and off-road autonomy. In this work, we evaluate both classical and Deep Learning (DL) based image registration techniques to access their suitability for off-road forestry applications. NeMAR, trained under 6 different configurations, demonstrates partial success however, its GAN loss instability suggests challenges in preserving geometric consistency. MURF, when tested on off-road forestry data shows promising large scale feature alignment during shared information extraction but struggles with fine details in dense vegetation. Even though this is just a preliminary evaluation, our study necessitates further refinements for robust, multi-scale registration for off-road forest applications.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.11952v1</id>\n <title>Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments</title>\n <updated>2026-03-12T14:00:20Z</updated>\n <link href='https://arxiv.org/abs/2603.11952v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.11952v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>RGB-NIR image registration plays an important role in sensor-fusion, image enhancement and off-road autonomy. In this work, we evaluate both classical and Deep Learning (DL) based image registration techniques to access their suitability for off-road forestry applications. NeMAR, trained under 6 different configurations, demonstrates partial success however, its GAN loss instability suggests challenges in preserving geometric consistency. MURF, when tested on off-road forestry data shows promising large scale feature alignment during shared information extraction but struggles with fine details in dense vegetation. Even though this is just a preliminary evaluation, our study necessitates further refinements for robust, multi-scale registration for off-road forest applications.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-12T14:00:20Z</published>\n <arxiv:comment>Preliminary results</arxiv:comment>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Pankaj Deoli</name>\n </author>\n <author>\n <name>Karthik Ranganath</name>\n </author>\n <author>\n <name>Karsten Berns</name>\n </author>\n </entry>"
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