# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 3 Experiments
### Ablation Study  
Ablation studies were conducted on each module of the proposed method, with all experiments performed on the TZMD_NUC dataset. As shown in Table 3, adding the spatiotemporal clue-gated encoder resulted in a 0.6% improvement in mIoU. This improvement was achieved by stacking multiple frames of point cloud data and encoding the spatiotemporal features, demonstrating that effectively mining and encoding the potential spatiotemporal clues from historical data can significantly enhance the model's performance. Further incorporation of the spatiotemporal feature select state-space module, which provides more comprehensive context integration of long-term spatiotemporal features aggregated by the spatiotemporal clue-gated encoder, led to an additional 1.3% increase in mIoU. This result highlights that effectively modeling long-term dependencies within spatiotemporal features enables for a more complete representation of spatiotemporal information, thereby further improving the model's semantic segmentation capability.  
Additionally, an ablation experiment was conducted to investigating the performance of the spatiotemporal clue-gated encoder with different numbers of stacked input frames, as shown in Figure 5. As the number of input frames increased, the model's performance peaked at three frames, then plateaued. This suggests that the spatiotemporal clue-gated encoder can efficiently extract key spatiotemporal clues from a small number of historical frames, making optimal use of limited input information. In comparison with the strategy of directly stacking more frames, using the spatiotemporal clue-gated encoder reduces the reliance on a large number of historical frames, saving storage and computational resources while maintaining good performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Close (<20 m)**} & \multicolumn{3}{c}{**Medium (\(\geq\)20 m, \(<\)50 m)**} & \multicolumn{3}{c}{**Far (>50 m)**} \\ \cline{2-9}  & **mIoU** & **Recall** & **Precision** & **mIoU** & **Recall** & **Precision** & **mIoU** & **Recall** & **Precision** \\ \hline LiMoSeg [9] & 70.35 & 77.64 & 87.74 & 45.78 & 54.28 & 70.41 & 8.74 & 8.92 & 91.27 \\ MotionSeg3D [21] & 71.66 & 79.97 & 87.35 & 52.21 & 59.27 & 81.40 & 4.99 & 4.99 & 100.00 \\ InsMOS [35] & 75.29 & 88.78 & 83.21 & 57.67 & 66.81 & 80.84 & 10.88 & 10.89 & 98.63 \\ SphereFormer [15] & 81.08 & 85.30 & 92.73 & 68.70 & 70.43 & 79.47 & 47.25 & 48.71 & 95.78 \\ MF-MOS [36] & 79.31 & 84.98 & 92.23 & 54.67 & 64.10 & 78.81 & 47.97 & 50.08 & 91.94 \\ Baseline & 78.96 & 84.03 & 90.92 & 68.71 & 68.83 & 77.43 & 47.30 & 48.67 & 93.75 \\ Ours & 81.12 & 86.35 & 93.62 & 70.48 & 72.18 & 85.59 & 49.17 & 52.85 & 95.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with different methods at different distances on SemanticKITTI _val_ set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**Module**} \\ \hline
**Spatiotemporal Clue-Gated** & **Spatiotemporal Feature** & **mIoU** & **\(\Delta\)** \\
**Encoder** & **Select State-Space Module** & \\ \hline \(\times\) & \(\times\) & 81.5 & - \\ \(\surd\) & \(\times\) & 82.1 & 0.6 \\ \(\surd\) & \(\surd\) & **83.4** & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation results of each module.