# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 5 Discussion
### Impact of Poisson Reconstruction Parameters  
In this section, we explore the influence of Poisson reconstruction parameters on the overall quality and accuracy of the generated models. The primary parameters that govern Poisson surface reconstruction include the octree depth, which controls the level of detail in the model; the point weight, which affects how closely the model conforms to the input point cloud; and the smoothness factor, which helps regulate the balance between detail and surface regularity. Through extensive experimentation, we have identified octree depth as one of the most critical factors in determining the level of detail in the reconstructed model. The results of using different octree depths in the reconstruction process are illustrated  
Figure 20: Modeling results with different steps: (**a**) Do not use any point cloud processing steps. (**b**) Only perform point cloud extraction. (**c**) Perform point cloud extraction and completion. (**d**) Use all point cloud processing steps.  
in Figure 21. From top to bottom, the octree depths used for modeling are 9, 10, and 11, respectively.  
As the octree depth increases from 9 to 11, a clear progression in the level of detail captured by the Poisson reconstruction is observed. At octree depth 9, the model exhibits a relatively coarse surface representation, with limited geometric detail. The tunnel walls appear smooth, and finer features, such as surface irregularities and texture, are not well captured. This lower depth setting is suitable for applications where computational efficiency is prioritized over detailed surface accuracy. However, the trade-off is a simplified model that lacks precision in representing the tunnel's complex surface structure.  
At octree depth 10, the model begins to incorporate more detailed surface features. The geometry of the tunnel walls is more accurately represented, and previously smoothed areas now show some of the surface's natural irregularities. This depth strikes a balance between model detail and computational cost, providing a level of surface fidelity that is adequate for most applications where both efficiency and accuracy are required.  
By octree depth 11, the model reaches a high level of detail, capturing even finer surface textures and irregularities. The tunnel walls are more defined, with increased accuracy in representing the small-scale variations in the surface. However, this comes at a cost: a higher octree depth requires more computational resources, and the increased detail may introduce noise or redundant features, particularly in areas where the point cloud  
Figure 21: Mesh model of Tunnel a. Left shows the inside of the tunnel, while right shows the outside of the tunnel on each row. From top to bottom, the models progress from coarse to fine.  
data may be sparse or noisy. While the enhanced detail is valuable in scenarios requiring high-fidelity surface modeling, it also necessitates careful consideration of the trade-offs between accuracy and computational expense.  
In summary, increasing the octree depth in Poisson reconstruction leads to a progressively finer model, but also introduces greater computational demand and the potential for noise. Selecting an appropriate octree depth depends on the specific application requirements, with deeper reconstructions providing higher detail at the cost of increased processing time and potential overfitting to noise in the point cloud data.