Feature registration is a core problem in computer vision and machine learning techniques. In recent years, significant progress has been made in learning-based feature registration algorithms. However, such methods usually capture image-level features and strive to capture contour information, inadvertently neglecting the exploration of low-texture regions, while the complex model structure brings more parameters. Therefore, it remains a challenging task to effectively address low-texture feature registration under limited sample conditions. In this paper, we propose a fast and robust feature registration algorithm to achieve fast feature registration in the low-texture case. Specifically, we first construct a two-step learning framework and use a faster adaptive heterogeneous filter (AdaH-Filter) to guide coarse-grained and fine-grained feature extraction. Then, we propose registration feature refiners (ReFR) to achieve the fusion of two-stage features in a low-texture environment while guaranteeing the speed of the detector. Finally, to further improve the network performance, a Warp Extractor is proposed to improve the speed and portability of model inference. This strategy allows the network to perform different levels of fine extraction adaptively according to the registration requirements. Moreover, the proposed method is trained in an end-to-end manner, guided by cosine loss, without any additional supervision. Extensive experimental results on several challenging and representative feature registration datasets in the industry show that the proposed method outperforms the existing state-of-the-art methods. More importantly, it has a faster inference speed under different sensor modalities.