## 3 Results
### Ablation Experiments of BMP_Net
#### 3.4.2 PSCDR Algorithm Parameter Ablation Experiments  
To investigate the impact of various parameters on the experimental results, multiple sets of parameter ablation experiments were conducted on the SGCBP dataset. Table 4 presents the ablation experiment results for the weight coefficients used in the similarity computation of Equation (3). In these experiments, four different values of \(\mu\) were selected: 0.01, 0.05, 0.1, and 0.5. The value of \(\lambda\) ranged from 0.001 to 0.009, with a step size of 0.002. The first weight coefficient \(\mu\) corresponds to the coefficient of the reciprocal of the cosine similarity plus two, while \(\lambda\) represents the weight coefficient for the Chamfer distance between point clouds. Due to the significant variability among negative point cloud samples, the computed Chamfer distance tends to be large and exhibits a wide range of values. Therefore, the range of \(\lambda\) was constrained based on preliminary experiments to ensure the overall value of Equation (3) did not become excessively large, and the optimal values reported in Table 4 were identified through these systematic ablation experiments. Through multiple experiments, it was observed that under different values of \(\mu\), when \(\lambda\) is greater than or equal to 0.01, all evaluation metrics result in NaN values, indicating that the value of \(\lambda\) should not be too large. As shown in the experimental results in the table, the best performance is achieved when \(\mu=0.05\) and \(\lambda=0.003\). Among the four sets of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Settings** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Ours** \\ \hline PFMixer & \(\times\) & \(\times\) & ✓ & ✓ & ✓ \\ Point Cloud Transition & & & & & \\ Downsampling & \(\times\) & ✓ & \(\times\) & ✓ & ✓ \\ Module & & & & & \\ N2P Attention & \(\times\) & ✓ & ✓ & \(\times\) & ✓ \\ RMSE & 213.47 & 160.76 & 172.59 & 97.67 & **75.92** \\ SGCBP & MAE & 165.41 & 137.07 & 151.04 & 83.05 & **63.19** \\  & MAPE & 0.301 & 0.265 & 0.274 & 0.142 & **0.115** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiments on different modules of BMP_Net. values, as \(\lambda\) increases, the overall evaluation metric values increase to varying degrees. This experimental result aligns with the hypothesis: when the value of \(\lambda\) is large, multiplying it with the computed Chamfer distance yields a significantly large value, which, when incorporated into the PSCDR method, may lead to numerical overflow and result in NaN values. Additionally, a larger \(\lambda\) value also contributes to an increase in the evaluation metric values. To investigate the impact of the temperature coefficient \(\tau\) on the PSCDR algorithm, temperature coefficient ablation experiments were conducted, with the results summarized in Table 5. As shown in the table, the PSCDR algorithm achieves optimal performance when \(\tau\) = 0.5. This value was determined through the systematic ablation experiments summarized in Table 5, which explored a range of values to balance model discriminative ability against numerical stability. Compared to image data, point cloud data typically exhibits higher dimensionality and complexity, necessitating a larger temperature parameter to ensure that the contrastive learning algorithm can effectively explore its data space. A larger temperature parameter smooths the overall output of \(\Lambda_{PSCDR}\), thereby enhancing the model's performance on point cloud data. However, for the PSCDR algorithm, both excessively high and excessively low temperature parameters can have detrimental effects. An overly high temperature parameter