## 4 Experiment
### Main Results  
#### 4.2.1 Pruned  
We evaluate the MinkUnet model and SPVCNN model on the 3D semantic segmentation dataset nuScenes-LiDARSeg [21] and SemanticKITTI [27]. nuScenes-LiDARSeg contains 1000 driving scenes from Boston and Singapore. The 20-second scenes are manually selected to show a diverse and interesting set of driving maneuvers, traffic situations, and unexpected behaviors. We train MinkUnet and its pruned model on the nuScenes-LiDARSeg/SemanticKITTI dataset and report the mean intersection-over-union (mIoU) on them. On the nuScenes-LiDARSeg dataset, the pruning ratio is set as 0.3 and 0.2 for the SemanticKITTI dataset because the SemanticKITTI dataset is denser than the nuScenes-LiDARSeg. All statistical results are listed in Tables 1 and 2, expressed as ratios against the results of the best baseline. In these tables, the bolded portions represent the differences between the pruned model and the best-performing model of that category. Negative values indicate a decline in performance, while positive values signify an improvement in performance. Due to its static nature, the background class is highly likely to be pruned, resulting in a noticeable decrease in the corresponding Intersection over Union (IOU). Compared to the benchmark, the IOU loss for background classes is approximately 10%. The observed results can be attributed to the substantial overlap within the background categories. For instance, in the nuScenes-LiDARSeg dataset, the \"Driveable surface\" class, and in the SemanticKITTI dataset, the \"Building\" and \"Road\" classes exhibit significant overlap. This overlap leads to a noticeable decline in accuracy for these categories. However, this pruning of the background creates opportunities for enhanced feature extraction from classes that comprise a smaller proportion of the point cloud data, thereby improving their accuracy. Subtracting the background allows the model to allocate more resources to the foreground classes, which often contain essential information for various tasks. Consequently, improving the IOU for these foreground classes can effectively compensate for the reduction of IOU for the background classes. This dynamic balance ensures that the mIOU remains nearly unchanged. This nuanced pruning strategy highlights the potential for selectively reducing background data redundancy while bolstering the accuracy of critical foreground categories. Such an approach emphasizes the importance of targeted pruning mechanisms that can judiciously differentiate between background and foreground points, thereby maintaining overall model performance while achieving greater computational efficiency. This balance is particularly crucial for applications requiring real-time processing and high precision, underscoring the need for sophisticated pruning algorithms to optimize both speed and accuracy. The decrease in background classes' IOU can be compensated by the improvement in foreground classes, making the mIOU nearly unchanged. As shown in Table 3, we use 23 scenes in nuScenes-LiDARSeg val split (about 900 contiguous keyframes) and 200 contiguous frames in SemanticKITTI val split to evaluate latency. On the SemanticKITTI dataset, MinkUnet (pruned) can achieve more than