# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 1 Introduction  
With developments in the field of automatic driving, the 2D object-detection network in the traditional image domain has increasingly failed to meet the safe driving requirements of autonomous vehicles in real-world scenes. When an autonomous vehicle drives in 3D space, it needs to recognize the 3D spatial information of obstacles to plan a safe path and avoid obstacles. Therefore, in the field of automatic driving, a powerful 3D object-detection network is needed to perceive and understand 3D scenes. The point cloud obtained by Lidar contains spatial geometric information of objects, which can be used to measure the shape and position of objects in 3D space. Therefore, the 3D object-detection network [1; 2] for Lidar point clouds is one of the most important research directions in this field. In general, 3D object detectors for Lidar are divided into single-stage detectors and two-stage detectors. The single-stage detector includes a preprocessing module, feature-extraction module, and detection head, while the two-stage detector includes an additional refinement module. Lidar provides disordered point clouds. If we directly extract the features of the point clouds, numerous query operations will result, with low efficiency. Figure 0(a),b show two commonly utilized methods for 3D object detection. In method (a) [3; 4; 5; 6], first, the 3D space is discretized into voxels of the same size, and then the attributes of the points in each voxel are extracted as features of the voxel. At the voxel level, the 3D convolution operation is used to extract the features of the voxels. Second, the convolved voxel features are projected onto the 2D BEV input to the RPN (region proposal network) to predict the 3D region of interest (ROI). For a two-stage detector, the ROI is also refined in the detection head toachieve better 3D object-detection results. Method (b) [7; 8; 9] encodes the point cloud and directly projects it to obtain a BEV feature map. After feature extraction by the 2D backbone, the BEV feature map is input to the detection head to obtain detection results. For certain reasons, most 3D object detection methods based on Lidar point clouds generate 3D object proposals using the 2D BEV feature map [10; 11; 12]. First, we can use or improve the mature 2D object-detection technology to extract the BEV feature map at a deeper level. Second, the projection of 3D objects (e.g., cars, pedestrians, and cyclists) is naturally separated in the BEV angle, and there is no serious occlusion problem. If we project in the X or Y direction, serious occlusion problems will occur in scenes with dense objects. Therefore, it is a good choice to use the 2D BEV feature map to predict 3D object proposals. Last, we perform similar operations without the occlusion problem in 3D space, but it requires considerable computation and time, and the inference efficiency is low. The 2D BEV feature map occupies a key position in the entire 3D object detector, so the amount of information it contains will directly affect the prediction performance of the detector. The current popular 3D object detection methods [10; 13] only use a single BEV feature map to predict 3D proposals. However, during the generation of BEV feature maps, voxel features are projected into the 2D plane, resulting in a significant reduction in the resolution of the \(z\)-axis and a compression of dimensions, which can lead to incomplete feature information in the 2D BEV feature map. Figure 1: Compared with (**a**) and (**b**), two commonly employed 3D object detection methods, our method (**c**) extracts features of different levels from two branches and fuses them to obtain a better BEV feature map. To solve the above problems and provide the missing information of the BEV feature map, as shown in Figure 1c, we propose a multi-view joint learning and BEV feature-fusion network that fuses the BEV feature map output from multiple modules to supplement the feature information of the BEV feature map.