# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 2 Related Work
### Voxel-Based Downsampling  
Voxel-based downsampling is a commonly used downsampling method first proposed by [PERSON] and [PERSON] [32] in 2001. The principle of voxel downsampling is to downsample the original point cloud by putting the 3D point cloud data into a 3D voxel grid, calculating the center of gravity within each voxel, and then using the center of gravity as the new sampling point. The advantage of voxel downsampling is the ability to convert original point cloud data into regular grid data, so that point cloud data can be processed and analyzed more conveniently [33]. At the same time, voxel downsampling can also adjust the accuracy and density of downsampling by changing parameters such as the voxel size and step size. This method is able to handle point cloud data sizes larger by orders of magnitude. Domestic and foreign researchers have also proposed many research methods based on voxel downsampling. [PERSON] [24] proposed a point cloud downsampling method based on hierarchical voxel segmentation, which balances point cloud downsampling by increasing the number of segmentation layers. A comparison of 3D point clouds before and after voxel downsampling is shown in Figure 3.  
In summary, while each of these downsampling methods has unique advantages, as shown in Table 1, they also present significant limitations, especially when dealing with the complexities of 3D point cloud data. Traditional methods like grid and cluster downsampling excel in noise reduction and feature preservation but often struggle with dense or irregularly distributed datasets, leading to potential loss of critical details. On the other hand, voxel-based downsampling provides a straightforward approach to regulate sampling density and maintain the overall shape and structure of point clouds. However, it tends to overlook fine local details and may introduce noise, underscoring the necessity for a more dynamic and adaptable solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline Deep learning-based downsampling & It can handle large-scale point cloud data efficiently and has fast processing speed. & It requires a significant number of training data and abundant computational resources. In cases of imbalanced training samples, it may lead to overfitting. \\ \hline Grid downsampling & (1) It can effectively remove noise and redundant information, yielding consistent sampling results. (2) Compared with voxel downsampling, it is better at preserving the local features of point clouds. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of various downsampling methods.  
Figure 3: Comparison of 3D point clouds before and after voxel downsampling. (**a**) Original point cloud image. (**b**) Voxel downsampling.  
Given these considerations, our proposed dynamic downsampling algorithm based on voxel filtering emerges as a powerful alternative. It is specifically designed to address the limitations of existing methods by efficiently processing high-density, large indoor scenes and preserving both the edge features and local detail information of 3D point cloud maps. This approach not only enhances the downsampling performance but also significantly reduces the need for manual parameter tuning, setting a new benchmark for the field.