# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 3 Experiments
### Segmentation Performance  
**TZMD_NUC dataset**. Table 1 shows a comparison of evaluation metrics between the proposed method and several mainstream semantic segmentation methods on our self-constructed TZMD_NUC mining dataset, including PointNet++ [14], Cylinder3D [8], PointNeXt [11], Retro-FPN [12], SphereFormer [15], and Mseg3d [20]. The proposed method outperforms recent methods such as Retro-FPN [12] and Mseg3d [20], indicating that leveraging spatiotemporal features can effectively improve model performance. Compared to SphereFormer [15], the proposed method improves by 1.3%, suggesting that the Mamba network performs better when handling spatiotemporal features than traditional attention mechanisms. Additionally, compared to the Cylinder3D [8] method, which directly stacks historical frame data, the proposed method shows a 3.9% improvement in mIoU, highlighting the superiority of the proposed spatiotemporal cue-encoding mechanism. PointNet++ [14] improves local feature learning but lacks deep modeling of the dependencies between local and global features. In contrast, the proposed method constructs a more comprehensive model of feature dependency from local to global, achieving a 4.0% performance improvement. Furthermore, the improved model shows a 2.1% increase over the baseline, further confirming the effectiveness of long-sequence spatiotemporal information in improving model performance in outdoor mining scenarios.  
**Different Distances on SemanticKITTI Dataset**. Due to the physical characteristics of LiDAR, point cloud sparsity increases with distance. In mining scenarios with few targets and high real-time demands, distant regions often lack valid point cloud data. The TZMD_NUC dataset focuses on nearby targets, whereas SemanticKITTI includes abundant long-range data. To evaluate the proposed method's robustness to point cloud sparsity, its performance on SemanticKITTI across different distance ranges is analyzed, as shown in Table 2. It can be clearly seen from the table that the increase in distance makes the recognition difficulty of the segmentation method further increase, and the performance of different methods in the three indicators of mIoU, recall (recall rate), and precision all decline. Compared with the baseline, our method achieves improvements across all metrics, with the mIoU at different distances increasing by an average of 1.9%. BEV projection-based method LiMoSeg [9] and distance projection method MotionSeg3D [21] both perform poorly at long distances. InsMOS [35] improves segmentation performance by fusing spatiotemporal features from multiple point cloud scan frames, but due to the lack of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mIoU** & **Mining Truck@IoU** & **Ground@IoU** & **Ore Pile@IoU** \\ \hline PointNet++ [14] & 79.4 & 79.6 & 79.4 & 79.3 \\ Cylinder3D [8] & 79.5 & 79.8 & 79.7 & 79.0 \\ PointNeXt [11] & 80.3 & 80.5 & 79.8 & 80.5 \\ Retro-FPN [12] & 82.4 & 82.4 & 82.6 & 82.2 \\ SphereFormer [15] & 82.6 & 83.2 & 82.4 & 82.3 \\ Mseg3d [20] & 81.8 & 82.1 & 81.5 & 81.8 \\ Baseline & 81.3 & 81.4 & 81.1 & 81.4 \\ Ours & 83.4 & 83.6 & 83.7 & 82.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with different methods on our self-constructed TZMD_NUC mining dataset.  
modeling of long-term spatiotemporal dependencies, it is inferior to the methods explored in this chapter in some indicators. MF-MOS [36] uses a projection branch in the designed dual-branch network structure, which leads to a certain degree of loss of three-dimensional geometric features, thereby limiting the extent of performance improvement. The network design in this chapter is completely based on a point-based method.