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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2603.11952 |
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| _version_ | 1866914387751600128 |
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| author | Deoli, Pankaj Ranganath, Karthik Berns, Karsten |
| author_facet | Deoli, Pankaj Ranganath, Karthik Berns, Karsten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11952 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments Deoli, Pankaj Ranganath, Karthik Berns, Karsten Computer Vision and Pattern Recognition 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. |
| title | Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.11952 |