# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 5 Discussion  
**Conclusions.** In this work, we propose a feature fusion network based on the Transformer architecture for unsupervised RGB-D point cloud registration. Compared to existing approaches, our method effectively captures global information, enabling features to perceive context more comprehensively and facilitating the learning of higher-quality matching relationships. Extensive experimental results demonstrate that our method achieved state-of-the-art performance on two datasets. Moreover, our approach exhibited superior adaptability to scenarios with varying frame intervals, making it highly applicable to engineering practices.  
**Future research directions.** Although the method proposed in this paper demonstrates practical applicability and effectiveness in solving real-world problems, several challenges remain to be addressed. In particular, with respect to model computational complexity, current algorithms incur significant computational overhead when dealing with large-scale datasets and complex scenarios, which limits their performance in real-time applications. Therefore, an important direction for future research is to further optimize the computational efficiency of the model while maintaining registration accuracy and robustness. In practical applications, especially in real-time-demanding scenarios such as autonomous driving and robotic navigation, the computational efficiency and real-time response capability of the algorithm are crucial. Thus, future work should focus on areas such as model lightweight, computation acceleration, and hardware adaptation to ensure that the proposed methods can meet real-time processing requirements and possess strong application potential.  
**Author Contributions:** Investigation, J.Q.; resources, L.L.; writing--original draft preparation, J.Q.; writing--review and editing, Z.H. and L.L.; visualization, J.Q.; project administration, Z.H. and J.Z.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript  
**Funding:** This research was funded by the Shanghai Science and Technology Innovation Project (Grant No. XTCX-KJ-2023-75, Grant No. XTCX-KJ-2024-24).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Parameters** & **FLOPs** & **Inference Time** \\ \hline PointMBF [24] & 5.483 M & 68.563 G & 0.1783 s/item \\ ours & 6.879 M & 78.809 G & 0.1925 s/item \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison of model parameters, computational complexity, and inference time between our method and PointMBF [24].  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The 3 DMatch dataset can be found in [https://3 dmatch.cs.princeton.edu/](https://3 dmatch.cs.princeton.edu/), and ScanNet dataset can be found in [[https://github.com/ScanNet/ScanNet](https://github.com/ScanNet/ScanNet)]([https://github.com/ScanNet/ScanNet](https://github.com/ScanNet/ScanNet)). The code will be released at [[https://github.com/yuyuyuy11/FFMT](https://github.com/yuyuyuy11/FFMT)]([https://github.com/yuyuyuy11/FFMT](https://github.com/yuyuyuy11/FFMT)), All accessed on 20 February 2025.  
**Conflicts of Interest:** The authors declare no conflicts of interest.