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.
Abstract (translated)
RGB-NIR（红绿蓝-近红外）图像配准在传感器融合、图像增强和非公路自主导航中扮演着重要角色。在这项工作中，我们评估了传统的基于深度学习（DL）的图像配准技术，以考察它们是否适用于非公路林业应用。NeMAR 在六种不同配置下训练后显示出部分成功，然而其生成对抗网络（GAN）损失不稳定的问题表明在保持几何一致性方面存在挑战。当 MURF 被测试用于非公路林业数据时，在共享信息提取期间显示出了大规模特征对齐的前景，但在密集植被中的细节处理上遇到了困难。尽管这只是初步评估，我们的研究仍需要进一步改进以实现适用于非公路森林应用的强大、多尺度配准技术。
URL
https://arxiv.org/abs/2603.11952
