# Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio
## 3 Experiment
### Results
#### 3.3.2 Feature Point Extraction Based on Adaptive Neighborhood Eigenvalue Loading Ratio  
To verify the accuracy and efficiency of the improved feature point extraction algorithm based on the adaptive neighborhood eigenvalue loading ratio proposed in this study, the algorithm in this study was compared with the planar feature point extraction algorithm proposed in the literature [26]. A comparison and analysis were conducted in various scenarios, and the feature results are displayed in Figure 6. In this figure, the blue dots represent the distribution of feature points extracted by the algorithm proposed in this study, denoted as Algorithm A, and the algorithm from the literature [26] is denoted as Algorithm B.  
Based on a qualitative analysis, the algorithm proposed in this study differs from the one proposed in the literature [26] greatly. As shown in Figure 6, although the feature points extracted by using the algorithm in the literature [26] are widely distributed, they are relatively sparse and not sufficiently representative. For instance, the ear features of the rabbit in scene (a) are not emphasized, and the contours were extracted ineffectively for scenes (c) and (d). This can result in the loss of specific details, or an overemphasis on minor variations, which has a potential impact on the model's quality and the usefulness of feature extraction. In contrast, the feature extraction algorithm proposed in this study exhibits higher point density in areas with prominent features, which better captures these geometric characteristics. The distribution of the blue points illustrates the natural smoothness of the object's contour shape. In scene (b), the contours of the dragon's head, scales, and tail are accurately extracted, with fewer feature points in the smoother areas of the dragon's body. In scenes (e) and (f), the home and regular object contours were extracted with high accuracy and completeness. In general, the algorithm proposed in this study outperforms the one in the literature [26] in terms of edge feature extraction for both regular planar point clouds and complex surface point clouds.  
Quantitatively, as shown in Table 2, the algorithm proposed in this study extracted many feature points for point clouds with plenty of curved and variable features, such as those of rabbits, dragons, and doll bears. In contrast, it extracted fewer feature points for point clouds with more planar features, such as tree benches, chambers, and offices. Additionally, because the algorithm proposed in this study employs a spherical neighborhood with an adaptive radius for neighborhood searches its time efficiency in most scenarios was, therefore, nearly double that of the algorithm proposed in the literature [26]. Specifically, in indoor scenarios, the time consumed by the algorithm proposed in this study was only 35.6% of that consumed by the algorithm proposed in the literature [26].  
Through comprehensive qualitative and quantitative analysis, the contour feature point extraction algorithm based on the eigenvalue loading ratio, as proposed in this study, demonstrates major advantages over the algorithm proposed in the literature [26] in terms of accuracy and timeliness.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Point Cloud**} & \multicolumn{5}{c}{**Preprocessing Result Data**} \\ \cline{2-6}  & **Original Point** & **Pass-Through** & **Filtration** & **Point Cloud** & **Average Raster** \\  & **Cloud** & **Filter** & **Ratio** & **Resolution** & **Point Cloud Density** \\ \hline Tree Stool-1 & 50,711 & 49,061 & 3.254\% & 0.006 & 4.602 \\ Lion-1 & 10,469 & 9750 & 6.868\% & 0.004 & 3.604 \\ Doll Bear-1 & 23,282 & 22,879 & 1.731\% & 0.003 & 1.596 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Point cloud adaptive raster filtering before and after comparison data table.