# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 4 Experiment and Result
### The Effect of PRF Module  
In the Point-Region Fusion (PRF) module, the parameter k represents the number of nearest neighbors considered around each point cloud data point during the K-Nearest Neighbors (KNN) algorithm. This parameter is crucial for the point cloud and image fusion process as it directly affects the fusion module's ability to capture local structures and features. When the value of k is small, the PRF module may only consider very local areas, potentially leading to fusion features that are not robust enough. The lack of sufficient neighboring points to construct a stable local environment might make the fused features highly sensitive to noise or deviations in individual points, thereby affecting the final detection performance. Conversely, if the value of k is set large, each point will consider a more extensive local area. This leads to spatially smoother fusion features that can resist noise and sparsity better. However, a k value that is too large can also introduce issues, such as blending irrelevant features, especially near the edges of objects where points from different objects might be erroneously considered together, thus reducing detection precision. Moreover, a large k value also increases computational complexity, impacting the efficiency of the algorithm.  
We compared the results of k_neighbors = 0 (\"no\" PRF), 3, 4, and 5 when the performance of the hardware device was supported. Through comparison, it can be found that PRF has a great effect on the improvement of detection performance. If k_neighbors = 4, the average value of AP3D increases by 1.53%. For k_neighbors = 5, the average APBEV increases by 0.84%. The specific effects are shown in Table 2. The effect of the PRF module is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**AP\({}_{\text{3D}}\) (\%)**} & \multicolumn{4}{c}{**AP\({}_{\text{BEV}}\) (\%)**} \\ \cline{2-9}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline MV3D & 71.09 & 62.35 & 55.12 & 86.02 & 76.90 & 68.49 \\ AVOD-FPN & 81.94 & 71.88 & 66.38 & 88.53 & 83.79 & 77.90 \\ ContFuse & 82.54 & 66.22 & 64.04 & 88.81 & 85.83 & 77.33 \\ CrossFusion & 83.20 & 74.50 & 67.01 & 88.39 & 86.17 & 78.23 \\ CLOCs & 87.50 & 76.68 & 71.20 & 92.60 & 88.99 & 81.74 \\ PI-RCNN & 84.59 & 75.82 & 68.39 & - & - & - \\ VPFNet & 88.51 & 80.97 & 76.74 & - & - & - \\ SFD & 91.73 & **84.76** & 77.92 & 95.64 & **91.85** & 86.83 \\ Ours & **91.95** & 80.56 & **78.08** & **95.75** & 88.83 & **88.68** \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**PRF**} & \multirow{2}{*}{**k\_neighbors**} & \multicolumn{4}{c}{**AP\({}_{\text{3D}}\) (\%)**} & \multicolumn{4}{c}{**AP\({}_{\text{BEV}}\) (\%)**} \\ \cline{2-9}  & & **Easy** & **Moderate** & **Hard** & **Average** & **Easy** & **Moderate** & **Hard** & **Average** \\ \hline No & - & 88.82 & 78.62 & 76.67 & 81.37 & 94.76 & 87.25 & 85.40 & 89.14 \\ \hline \multirow{3}{*}{Yes} & 3 & 90.70 & 79.68 & 77.24 & 82.54 (+1.17) & **95.15** & **88.40** & 86.28 & 89.94 (+0.80) \\  & 4 & **91.06** & **79