## 3 Results
### Comparative Experiments  
In addition to the models from the SGCBP paper, this study also included PointMixer [35], Point Transformer [36], and PointNeXt [37] as comparison models. It is important to note that in the SGCBP paper, the authors did not use the MAPE metric; therefore, the data presented in Table 1 from the SGCBP paper do not include MAPE values. PointMamba [38] was not included in the comparison because it focuses on classification rather than regression. While classical machine learning regressors (e.g., Random Forest, Support Vector Regression with hand-crafted features) represent an alternative approach, the scope  
Figure 7: Summary of the full PSCDR-BMPNet pipeline for biomass prediction. Raw LiDAR point clouds are preprocessed before entering the model. During the training phase, the PSCDR module provides a contrastive learning objective to guide the optimization of the BMP_Net deep regression network. For inference, the trained BMP_Net directly processes input data to generate the final biomass prediction. of this study was to benchmark against contemporary deep learning methods specifically designed for or adapted to point cloud data, which have generally demonstrated superior performance in capturing complex 3D spatial patterns for tasks like biomass estimation in the recent literature [9; 10; 11; 29; 30; 31; 32; 33]. Future comparative studies could incorporate such classical baselines for a broader perspective. As shown in Table 1, except for BMP_Net and BioNet, the PointMixer network performs the best among all the unimproved networks. Its RMSE, MAE, and MAPE were 113.41, 93.26, and 0.194, respectively. PointMixer excels in the biomass prediction task using point cloud data, primarily because of its universal and efficient structure, as well as its parameter efficiency and hierarchical response propagation advantages. Its versatility makes it suitable for various types of point cloud data, and the channel-mixing MLP effectively promotes feature aggregation. Additionally, its concise and efficient design improves the efficiency of processing large-scale data. The use of a symmetric encoder-decoder block with a hierarchical response propagation mechanism helps capture complex relationships within the point cloud data, thereby enhancing prediction performance. This result also validated our design choices for BMP_Net, which adapted and improved upon concepts from the PointMixer [35] architecture. The BMP_Net model proposed in this paper outperforms the BioNet model introduced in the SGCBP paper, showing better performance in RMSE, MAE, and MAPE, with values of 84.8, 67.31, and 0.131, respectively. BMP_Net effectively extracts and utilizes the relevant information from point cloud data by leveraging key components such as the Embedding layer, N2P Attention, PFMixer module, and point cloud downsampling. This significantly improves the accuracy and performance of biomass prediction tasks. The Embedding layer transforms high-dimensional point cloud data into a more manageable vector form, N2P Attention captures local features, the PFMixer module performs deep feature extraction and data compression, and point cloud downsampling reduces noise and redundant information. These components work together to provide a more robust decision-making foundation for biomass prediction tasks. After applying the PSCDR method, BMP_Net showed a reduction in RMSE, MAE, and MAPE, with decreases of 8.56, 4.12, and 0.016, respectively. This indicates that the PSCDR method plays a significant role in predicting biomass and other regression tasks with point cloud data. The combination of BMP_Net and PSCDR yields the best performance. PSCDR enhances the prediction process by introducing Chamfer distance to calculate the similarity in point cloud data in three-dimensional space while considering the similarity between features. This approach provides strong support for label comparison and label value prediction, contributing to the improved performance of deep regression tasks.