# Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments

arXiv: https://arxiv.org/abs/2603.11952 · HTML: https://mlsanity.com/paper/2603.11952/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020)
- [DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior](https://mlsanity.com/paper/2303.06834/) (2023)
- [Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision](https://mlsanity.com/paper/2411.18025/) (2024)
- [Towards RGB-NIR Cross-modality Image Registration and Beyond](https://mlsanity.com/paper/2405.19914/) (2024)

## Abstract (arXiv)

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.