## 3 Method
### RS-Aug Algorithm  
The widely used GT-Aug [9] overlooks the semantic information of the scene during random sampling and augmentation. Consequently, it might introduce ground truths into unreasonable areas, resulting in unreasonable scenes [24]. These unreasonable augmentations could mislead networks to learn incorrect information. Here, we propose RS-Aug, which considers the semantic information of the scene in augmenting the samples, as depicted in Figure 6a. As shown in Figure 6a, RS-Aug includes 6 steps. 1. Establish the database. Establish a database that includes all the ground truths (bounding boxes and the points inside them). For instance, in our following experiment, three categories of ground truths are included. They are vehicles, cyclists, and pedestrians. 2. Segment the ground and non-ground points. Input a training sample, then apply the RANSAC algorithm to segment the non-ground points and ground points and obtain ground fitting parameters. 3. Points clustering. Utilize the DBSCAN algorithm to cluster non-ground points, thereby obtaining clusters of non-ground points. 4. Determine the placement area. Obtain the semantic information of the current scene through Steps 2 and 3, classifying the point cloud into ground and non-ground points. Subsequently, apply the Minimum Bounding Rectangle algorithm to fit the bounding boxes of the non-ground point clusters, acquiring the position and size of the bounding boxes. Using the size and position information, exclude the regions occupied by the bounding boxes from the ground area identified in Step 2, leaving the remaining space as the designated area for placement. 5. Construct a new sample. Randomly select the ground truths from the database based on the proportions of different categories appearing in the training dataset. Then, randomly insert ground truths into the designated placement area. 6. Collision checking. Check whether the newly placed point cluster collides with the existing point clusters. If a collision is detected, repeat step (5) and then perform the  
Figure 6: Flow charts of RS-Aug (**a**) and GT-Aug (**b**). The bold boxes indicate the differences between the two algorithms, including “Points segmentation”, “Points clustering”, and “Determine the placement area” versus “Randomly select the placement area”. collision check again. If no collision is detected, the enhanced data will be fed into the network for training, and this process ends. By following the steps above, in a road environment, RS-Aug places ground truths on paved surfaces while avoiding existing objects. Conversely, in non-road environments, ground truths are placed on flat terrain while avoiding the occlusion of existing objects. Benefiting from these measures, RS-Aug ensures that the augmented samples are reasonable. There are differences between RS-Aug and GT-Aug. As shown in Figure 5(a), GT-Aug lacks the operations of \"Points segmentation\", \"Points clustering\", and \"Determine the placement area\", which correspond to Steps 2, 3, and 4 mentioned previously. Therefore, RS-Aug can selectively place ground truths into a scene based on the semantic information of the point cloud scene, whereas GT-Aug can only randomly place ground truths into a scene without being able to judge the reasonableness of the placement. To demonstrate the effects of RS-Aug and the shortcomings of GT-Aug, a comparison is presented based on PointPillars. This comparison is conducted on a single sample from the KITTI 3D object dataset [31]. In this instance, six cyclists and pedestrians are added to the current scene. Figure 6(a),b show the image and point cloud of the original scene, respectively. Figure 6(c),d illustrate the augmented results. It can be seen that some pedestrians and cyclists (green and blue boxes) have been inserted into the scene. However, as shown in Figure 6(c), GT-Aug places some pedestrians and cyclists outside the road (highlighted with black boxes), failing to accurately reflect actual driving scenarios.