# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 1 Introduction  
As the cornerstone of future transportation systems, autonomous driving technology increasingly demands sophisticated vehicle perception and environmental understanding. Within this context, 3D object detection plays a decisive role as the critical component [1, 2, 3]. The rise of autonomous driving technology sparks an urgent need for efficient and accurate object detection methods. In complex road environments, object detection necessitates not only real-time capabilities but also a high degree of recognition for objects of various sizes and densities.  
Cameras, serving as the quintessential data acquisition sensors in the realm of two-dimensional and three-dimensional object detection endeavors, furnish imagery enriched with intricate texture information encapsulated in RGB images [4, 5, 6]. These images are characterized by their high-resolution attribute, yet they are concurrently marred by the deficiency of depth perception, a limitation that substantially hampers the attainment of precise three-dimensional localization [7, 8]. As a common representation of 3D scenes, point cloud data plays an important role in object detection. Point cloud data is obtained by LiDAR. Three-dimensional object detection based on LiDAR has received much attention because of its ability to provide accurate depth information, which is essential for accuratespatial positioning. However, LiDAR data often has sparsity issues [9], especially in complex environments, which can limit its effectiveness in detecting small or distant objects [10]. Small object detection and feature extraction are also a challenge in the field of 3D object detection. Small objects often have a low signal-to-noise ratio and fuzzy appearance characteristics, which makes them more likely to be misclassified or missed. The existing methods face some difficulties in identifying and locating small objects because they fail to extract and utilize the key feature information of small objects effectively.  
Conventional methods of fusion often prove inadequate in addressing these challenges, as they neither sufficiently enhance the representational capabilities of point clouds nor effectively maintain and amplify the feature information of small objects during the fusion process. In response to these challenges, this paper introduces a 3D object detection network based on point cloud and RGB images. The network utilizes point cloud data from a 64-beam LiDAR and RGB images from a monocular camera to achieve vehicle detection. The principal contributions of this study are as follows:  
1. This study introduces a novel point-region fusion module (PRF) designed to integrate region-specific features from images with corresponding point clouds, thereby enhancing the performance of the fusion process.
2. This study develops an image feature fusion module (IF-Fusion), which ingeniously combines image feature maps of various sizes. This approach is specifically aimed at preserving the features of small objects while augmenting the overall expressive capability of the point cloud features.
3. Experimental results based on the KITTI benchmark demonstrate that the proposed method achieves significant advancements over previous fusion networks. This underscores the efficacy and innovative nature of the proposed approach in the realm of point cloud object detection.  
In this paper, Section 2 discusses the related work in the field of 3D object detection. Section 3 presents the comprehensive framework of the network. Section 4 details the experimental protocols, conducts ablation studies and analyses the results. Finally, Section 5 concludes this study and outlines projections for future research directions.