# PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration
## 5 Conclusions  
In this paper, we have presented a new end-to-end point cloud registration network that considers both local and global characteristics of point clouds. By combining local feature encoding and an attention module, our model effectively extracts the corresponding information of the input point cloud, with the learned features greatly enhancing the point cloud registration. In addition, our method shows strong performance on multiple tasks. Especially in the case of the multi-object class without noise, the rotation average absolute error of PTRNet is reduced to 1.601 degrees and the translation average absolute error is reduced to 0.005 units. Experimental results prove that the proposed method is effective and robust compared with the contrast method. Furthermore, PTRNet is easily integrated with other processes and adaptable to many needs. However, the PTRNet model has high complexity, and the registration speed needs to be optimized. In the future, we plan to modify the network and simplify the model, so as to complete point cloud processing tasks such as cross point source registration and collective registration of multiple point clouds on the basis of PTRNet while realizing efficient registration.  
Writing--review and editing, C.L. and L.S.; writing--original draft preparation, S.Y.; visualization, Y.L. ([PERSON]); supervision Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MSE (R)** & **RMSE (R)** & **MAE (R)** & **MSE (0)** & **RMSE (0)** & **MAE(0)** \\ \hline iPCRNet & 1.274 & 1.129 & 2.771 & 5.700 & 2.388 & 0.016 \\ DCP-V2-MLP & 679.070 & 26.059 & 22.583 & 0.139 & 0.373 & 0.295 \\ ICP & 10.446 & 3.232 & 4.760 & 0.000 & 0.010 & 0.015 \\ Model-V1 & 1.134 & 1.065 & 2.767 & 0.193 & 0.439 & 0.006 \\ Model-V2 & 3.315 & 3.027 & 2.125 & 0.018 & 0.134 & 0.012 \\ PTRNet & 1.242 & 1.821 & 1.936 & 0.001 & 0.032 & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Same Object Category With Noise.  
Figure 4: Test time (ms) of different methods.  
**Funding:** This research was funded by [the Network Collaborative Manufacturing Integration Technology and Digital Suite Research and Development Project of the Ministry of Science and Technology] grant number [2020 YFB1712401]; [the Key Scientific Research Project of Colleges and Universities in Henan Province] grant number [21A520042]; [Major public welfare projects in Henan Province] grant number [201300210500] And The APC was funded by [the Network Collaborative Manufacturing Integration Technology and Digital Suite Research and Development Project of the Ministry of Science and Technology].  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Datasets that support reporting results can be found in the link: [[https://drive.google.com/drive/folders/19X68](https://drive.google.com/drive/folders/19X68) jeeXdeZgFp3 cuCVpaca4 LLw48 HZ7 usp=sharing]([https://drive.google.com/drive/folders/19X68](https://drive.google.com/drive/folders/19X68) jeeXdeZgFp3 cuCVpaca4 LLw48 HZ7 usp=sharing) (accessed on 9 January 2022).  
**Acknowledgments:** This work was supported by [the Network Collaborative Manufacturing Integration Technology and Digital Suite Research and Development Project of the Ministry of Science and Technology] grant number [2020 YFB1712401]; [the Key Scientific Research Project of Colleges and Universities in Henan Province] grant number [21A520042]; [Major public welfare projects in Henan Province] grant number [201300210500]. We thank LetPub (www.letpub.com (accessed on 9 January 2022)) for its linguistic assistance during the preparation of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.