# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 5 Conclusions  
In this paper, we proposed PRNet, a novel 3D object detection network that leverages the fusion of point cloud and image data to address the challenges of detecting objects with sparse point clouds and small objects. The key contributions of our work include the introduction of the Point-Region Fusion (PRF) module, which effectively integrates local point cloud features with regional image features, and the Image Feature Fusion (IF-Fusion) module, which enhances the network's ability to detect small objects through multi-scale feature fusion. Experimental results on the KITTI benchmark demonstrate that our approach achieves state-of-the-art performance, particularly in scenarios involving sparse point clouds and complex environments. The ablation studies further validate the effectiveness of both the PRF and IF-Fusion modules, highlighting their complementary roles in improving detection accuracy. Our method not only addresses the limitations of traditional single-modality approaches but also provides a robust framework for multi-modal data fusion in 3D object detection. However, there are some limitations to this article's network. For instance, the fusion process may still lose some fine-grained information, especially for distant objects. Additionally, the computational complexity of the KNN algorithm in the PRF module could be a bottleneck for real-time applications. Future work will focus on addressing these limitations by exploring more efficient fusion strategies and extending the evaluation to larger datasets. There are several promising directions for future research. First, more advanced fusion strategies could be explored to further reduce information loss and improve the integration of point cloud and image features. Second, extending the evaluation to larger and more diverse datasets would help validate the generalizability of our approach. Finally, the practical deployment of our method in real-world applications, such as autonomous driving and robotic navigation, presents unique challenges that warrant further investigation. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**PRF**} & \multirow{2}{*}{**IF-Fusion**} & \multicolumn{4}{c}{**AP\({}_{\text{3D }}\) (\%)**} & \multicolumn{4}{c}{**AP\({}_{\text{BEV}}\) (\%)**} \\ \cline{3-8}  & & & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline Lidar & \(\times\) & \(\times\) & 88.82 & 78.62 & 76.67 & 94.76 & 87.25 & 85.40 \\ \hline Lidar + Image & \(\surd\) & \(\times\) & 90.70 & 79.68 & 77.24 & 95.15 & 88.40 & 86.28 \\ Lidar + Image & \(\times\) & \(\surd\) & 90.70 & **81.10** & **79.00** & 94.79 & 88.08 & 86.10 \\ Lidar + Image & \(\surd\) & \(\surd\) & **91.95** & 80.56 & 78.08 & **95.75** & **88.83** & **86.72** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation Study. The best scores are highlighted in bold. “\(\surd\)” indicates that the module is added, and “\(\times\)” indicates that the module is not added. **Author Contributions:** Conceptualization, Y.F. and H.H. ; Methodology, Y.G. ; software, Y.F. ; Validation, Y.F. and Y.G. ; Formal analysis, H.H. ; Investigation, H.H. ; Resources, Y.G. ; Data curation, Y.G. ; Writing--original draft preparation, Y.G. and H.H. ; Writing--review and editing, Y.F. ; Visualization, Y.G. ; Supervision, H.H. ; Project administration, Y.F. ; Funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was supported by the National Natural Science Foundation of China under Grant Nr. : 61961020. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The code is available at [[https://github.com/JackKu0/PRNet](https://github.com/JackKu0/PRNet)]([https://github.com/JackKu0/PRNet](https://github.com/JackKu0/PRNet)) (accessed on 10 March 2025).