对比传统与深度学习方法，评估多光谱图像配准在林地环境中的适用性。
Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments
- 测试了多种经典与深度学习配准方法在林地场景的表现。
- NeMAR在6种配置下部分成功，但生成对抗损失不稳定影响几何一致性。
- MURF能有效对齐大尺度特征，但在密集植被中难以捕捉细节。
RGB-NIR图像配准在传感器融合、图像增强和非公路自主驾驶中至关重要。本文评估了经典与基于深度学习的图像配准技术在非公路林地应用中的适用性。NeMAR在6种不同配置下表现出部分成功，但其生成对抗网络损失的不稳定性表明在保持几何一致性方面存在挑战。MURF在非公路林地数据上测试时，展现了共享信息提取中的良好大尺度特征对齐能力，但在密集植被区域的细粒度细节处理上表现不佳。尽管仅为初步评估，本研究仍强调需进一步优化以实现鲁棒的多尺度图像配准，适用于非公路林地场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。