# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 2 Related Work  
### Methods Based on Single-Modality  
The point cloud offers a fully three-dimensional reconstruction of the scene, providing rich geometric, shape and scale information [11]. In the realm of image-based 3D object detection, networks are categorized into two types: 3D object detection networks based on result mapping [12; 13; 14; 15; 16; 17] and 3D object detection networks based on feature mapping [18; 19; 20]. The 3D object detection networks based on result mapping typically first predict semantic information from images, such as two-dimensional bounding boxes, three-dimensional sizes, and distances of the objects. Then, by utilizing the constraint relationships between 2D and 3D spaces, the aforementioned predicted results are mapped into three-dimensional space, thereby obtaining the three-dimensional bounding boxes of the objects. The Deep3 DBox proposed by Mousavian et al. [12] uses the constraint relationship that \"the perspective projection of the two-dimensional boundary box in the three-dimensional space should be close to at least one edge of the two-dimensional boundary box\" to estimate the pose and size of the three-dimensional boundary box and realize three-dimensional object detection. This kind of network has the advantage of fast processing speed, but the detection of 3D coordinates is very limited. The 3D object detection networks are based on feature mapping. Generally, they first map 2D image features to 3D space and then perform 3D object detection based on 3D spatial features. Several researchers [18; 19] start by estimatingthe depth of images and then use this depth information to project corresponding pixels into 3D space, creating a Pseudo-Point cloud. Subsequently, this information is used for 3D object detection. Compared with the previous kind of network, this kind of network provides more abundant spatial structure information and improves detection accuracy and robustness. However, the accuracy of depth estimation is not enough, and it also brings a large amount of calculation. Several researchers have exclusively utilized LiDAR point clouds for 3D object detection, considerably enhancing task precision. They employ point-based representation learning strategies [21; 22; 23], which directly derive features from the raw point clouds. Conversely, other researchers transform these point clouds into standard 2D grid representations [24; 25; 26] or 3D voxel representations [27; 28; 29; 30; 31; 32; 33]. Shi et al. [34] employed PointNet++ as their feature extraction network. Using this, they generated Regions of Interest (ROIs) and conducted parameter regression for accomplishing the 3D object detection task. Li et al. [35] projected LiDAR point clouds onto a front-view perspective to acquire a 2D grid. However, this front-view 2D grid encounters significant occlusion and overlap challenges. Yang et al. [36; 37] introduced HDNet and PIXOR, which project LiDAR point clouds onto a bird's-eye view (BEV) to create a 2D grid. Zhou et al. [27] pioneered VoxelNet, an end-to-end trainable deep neural network. This network transforms LiDAR point clouds into uniformly distributed voxels in 3D space, utilizing conventional 3D CNNs for feature extraction. Most of the three-dimensional object detection networks represented by points rely on Pointnet++ [38], but Pointnet++ cannot encode complex geometric information of the point neighborhood, and the importance of each point in the neighborhood is ignored in the coding process, resulting in feature redundancy or loss. The grid representation 3D object detection network will lose the projection dimension information in the point cloud projection process, so the accuracy of this kind of network is usually not high. The detection accuracy of voxel representation 3D object detection network is at a high level among all 3D object detection networks.