# PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration
## 4 Results
### Robustness Experiment  
To evaluate the robustness of the network to noise, we performed experiments on the point clouds with Gaussian noise. Using the dataset described in Section 4.1, we sampled the Gaussian noise independently from N (0, 0.01), clipped the noise to [\(-\)0.05, 0.05], and added it to source point clouds during testing. In this experiment, we trained the iterative PCRNet, DCP-V2-MLP, Model-V1, and object categories with noise.  
We compared the accuracy of ICP, iterative PCRNet, DCP-V2-MLP, Model-V1, and Model-V2 with noise in the source point clouds to ensure that the data set had the same source and template point cloud pairs for the sake of a fair comparison. As shown in Table 3, the registration errors of each method are affected to varying degrees. Compared with experiment 4.1, the MAEs of iPCRNet and ICP methods are reduced, among which iPCRNet, which may benefit from the lightweight characteristics of its network, while ICP has reduced its error in this experiment, but it is the same as the DCP-V2-MLP, their registration effect is not ideal, which may be due to the fact that the ICP method easily falls into local optimum.. At the same time, the registration effects of Model-V1, Model-V2 and PTRNet are all disturbed by noise, which we think may be related to the complexity of the network. However, the MAEs of our model were the smallest.  
Table 4 shows the training test results on the same object category. In this test, the data set described in Section 3 was used, with Gaussian noise added to the source point cloud as described above. We trained the networks on a specific object class as in other research [21], and tested them on the same category using 150 vehicle types. Gaussian noise was added to the source point cloud during training and testing. As shown in Table 4, compared with multi-object classes with noise, the errors of each method are reduced. It is worth noting that the rotation error and translation error of Model V2 are reduced by 47%  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MSE (R)** & **RMSE (R)** & **MAE (R)** & **MSE (t)** & **RMSE (t)** & **MAE(t)** \\ \hline iPCRNet & 1.220 & 1.104 & 2.715 & 7.844 & 2.801 & 0.006 \\ DCP-V2-MLP & 919.594 & 30.325 & 24.013 & 0.046 & 0.214 & 0.170 \\ ICP & 9.638 & 3.105 & 4.587 & 6.509 & 2.551 & 0.017 \\ Model-V1 & 4.180 & 2.045 & 2.057 & 0.023 & 0.152 & 0.007 \\ Model-V2 & 1.315 & 1.146 & 1.976 & 0.158 & 0.397 & 0.010 \\ PTRNet & 1.143 & 1.069 & 1.601 & 0.003 & 0.055 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Same Object Category Without Noise.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MSE (R)** & **RMSE (R)** & **MAE (R)** & **MSE (t)** & **RMSE (t)** & **MAE(t)** \\ \hline iPCRNet & 1.973 & 1.405 & 3.518 & 1.863 & 1.365 & 0.013 \\ DCP-V2-MLP & 779.542 & 27.920 & 23.173 & 0.084 & 0.290 & 0.225 \\ ICP & 9.638 & 3.105 & 4.587 & 6.509 & 2.551 & 0.017 \\ Model-V1 & 7.003 & 2.646 & 2.907 & 0.239 & 0.489 & 0.015 \\ Model-V2 & 4.273 & 2.067 & 4.012 & 0.000 & 0.010 & 0.020 \\ PTRNet & 1.340 & 1.158 & 2.739 & 0.036 & 0.190 & 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multi-Object Classes With Noise.  
and 40%, respectively, far more than that of Model-V1, which also confirms the viewpoint in Experiment 4.2. That is, local features have less effect on the same category registration than global features, and PTRNet had the strongest resistance to Gaussian noise and the best registration results.