## 4 Experiments
### Motion Vector Estimation  
Because the SemanticKITTI dataset only contains semantic label information, pose information, and sensor calibration information, the ground truth motion vector of the moving object cannot be directly obtained according to the data provided by SemanticKITTI. In this paper, the KITTI tracking dataset is used to construct a motion vector evaluation dataset that is more consistent with the motion of point cloud instances, and the nuScenes dataset is also tested. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Time (ms) \(\downarrow\)** & **Fitness Score \(\downarrow\)** & **Displacement Error (m) \(\downarrow\)** & **Angular Error (rad) \(\downarrow\)** \\ \hline GICP (Single thread) & 122.868 & 0.204376 & 0.0102 & 0.0014 \\ GICP (Multi-thread) & 17.959 & 0.204384 & 0.0102 & 0.0014 \\ VGICP (Single thread) & 93.106 & 0.205022 & 0.0134 & 0.0019 \\ VGICP (Multi-thread) & 13.549 & 0.205022 & 0.0134 & 0.0019 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Structure** & \(IoU^{MOS}\) & **FPS** & **Achieve Lidar Scanning Speed** \\ \hline PointNet [6] & Point & 0.16 & 3.65 & \(\times\) \\ MINet [30] & RV & 0.19 & 10.84 & ✓ \\ MINet/N = 10 & RV & 0.31 & 9.5 & \(\times\) \\ SalaSNext [15] & RV & 0.46 & 12.64 & ✓ \\ SalaSNext/N = 10 & RV & 0.62 & 3.41 & \(\times\) \\ LMNet [4] & RV & 0.58 & 75.13 & ✓ \\ LMNet/N = 10 & RV & 0.66 & 19.60 & ✓ \\ EmPointMovSeg [31] & Point & 0.43 & 8.74 & \(\times\) \\ Cylinder & Voxel & 0.61 & 8.03 & \(\times\) \\ MotionSeg3D [16] & RV & 0.71 & 8.55 & \(\times\) \\ \hline Ours & RV & 0.65 & 54.72 & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of moving object segmentation on SemanticKITTI. Specifically, in order to obtain the ground truth of the instance motion, the ground point cloud is removed by a predetermined height threshold. The corresponding point cloud covered by the FOV of the front camera is obtained. The motion vector of the moving instance in the point cloud is measured based on the tracking box of the instance. The instances whose motion vectors are less than a certain threshold are marked as static objects. The ground truth of the motion vector for each instance is set as  
\[V_{GT}=\{T(P_{i}^{t+1}-P_{i}^{t})\}_{i=1}^{I} \tag{12}\]  
where \(T\) represents the time offset between two adjacent frames and \(P_{i}^{t}\) represents the position of the center point of the \(i\) instance at time \(t\). In the experiments, the keyframe is set five frames apart. To accurately segment the instance from moving point clouds, the \(\epsilon\) is essential in principle. However, in actual experiments, different instances are usually far away from each other, while the instance locations are continuously changing and close to each other at different times; so, we found that e values from 0.1 m to 0.4 m can achieve accurate instance segmentation. The segmentation accuracy reaches 100% in all 10 randomly selected datasets, and \(\epsilon\) is set to 0.2 m in this paper. The errors of the estimated vectors on the three axes for the KITTI and nuScenes datasets are listed in Table 6. It can be observed that the errors in the X-axis and Y-axis are between 0.01 m and 0.02 m. However, the error in the Z-axis is between 0.02 and 0.04. The main reason is that Z-axis is in the vertical direction, and the matching result is not good enough in the vertical direction due to the low resolution of Lidar in the vertical direction. By combining the segmentation labels and moving vectors, the prediction results can be converted into typical scene flow results. As shown in Table 7, the results of the proposed method and the classical scene flow methods in the scene flow evaluation metrics EPE3D [19] can be compared. EPE3D is the average 3D end point error, that is the average Euclidean distance between all estimated motion vectors and the ground truth motion vectors.