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

Pankaj Deoli; Karthik Ranganath; Karsten Berns

cs.CVMar 12, 2026

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

Authors: Pankaj Deoli, Karthik Ranganath, Karsten Berns

Organizations: Robotics Research Laboratory, University of Kaiserslautern-Landau (RPTU, Kaiserslautern, Germany) · The work done by Karthik Ranganath is associated with their Master Thesis with the lab.

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.

Figures & tables

Configs	Batch Size	Learning Rate	Image Size	Epochs	GAN Mode
Config 1	32	0.0002	286p	100	Vanilla
Config 2	32	0.0002	600p	100	Vanilla
Config 3	12	0.0001	600p	200	Vanilla
Config 4	12	0.0001	600p	200	LSGAN
Config 5	64	0.0001	600p	100	Vanilla
Config 6	12	0.00005	960p	50	Vanilla

TABLE I: Training Configurations for NeMAR [ 12 ]

Panel of Table 2 — TABLE II: Results of different training configurations on NeMAR [ 12 ]

Panel of Table 3 — TABLE III: Comparison of Different Image Registration Methods

Panel of Table 4 — TABLE IV: Comparison of Different feature matching Methods

Cross-Modal Image Registration

Cross-modal image registration aligns images from different sensors or modalities into a shared spatial frame.

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Conventional image registration algorithms are robust to domain shifts and achieve low errors, but they are slow and computationally expensive. Deep-learning methods are efficient at inference-time, but face challenges in out-of-domain samples. We propose Recursive Uncertainty-Gated Image Registration (RUGI), an algorithm for iteratively refining deformation fields predicted by learning-based registration models. At each iteration, the registration model predicts an incremental deformation, and a gating map modulates the update. Refinements are hence concentrated in regions that remain difficult to register. We explore two gating strategies: a learned uncertainty-based approach and an image residual error approach. We evaluate RUGI on cardiac MRI and echocardiography datasets and show consistent improvements over single-step inference. Ablation experiments demonstrate that iterative refinement alone improves registration, but informative spatial gating provides a significant additional benefit. The error-gated variant of RUGI can also be applied directly to existing pretrained models; applied to VoxelMorph, TransMorph, and CycleMorph, it yields MSE reductions of 27-37% with no modification to the original training procedure. The improvements in registration performance are reflected in decreased errors in ejection fraction estimation relative to ground truths. These results demonstrate that spatially selective iterative refinement provides an effective strategy to improve registration accuracy at inference-time.

Abstract

Cross-Modal Image Registration

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