# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 2 Related Work  
In recent years, various 3D object-detection networks have been introduced. Generally, according to different feature extraction methods, existing networks are divided into the following categories: point-based methods, voxel-based methods, pseudo-image based methods, and hybrid methods. **Point-based 3D detection:** A point cloud can directly represent the underlying spatial geometric information of an object and has the characteristics of sparsity, irregularity, and disorder. Therefore, in a point-based network, feature extractors need to be able to learn features from the original points. Because traditional feature extraction methods, such as CNN, cannot be directly used for irregular original point clouds, most point-based methods directly extract features point by point. PointRCNN [14] extracts features point by point, divides the point cloud into front attractions and background points, and then uses a bottom-up approach to generate high-quality 3D region recommendations for each front attraction. Point-GNN [15] directly uses a graph neural network on a point cloud to retain the irregularity of the point cloud and then extracts the point cloud features by iterating vertex features on the same graph. Point-based feature extractors can retain high-resolution 3D structure information, but point-by-point feature extraction will incur high computing and time costs. 3 DSSD [16] proposes a lightweight single-stage target detector that uses a fusion sampling strategy to ensure sufficient internal points in the foreground instance, and then designs an anchor-free frame-prediction network to meet the requirements of high accuracy and speed. **Voxel-based 3D detection**: Voxelization divides a point cloud into evenly spaced voxel grids, converts irregular shape data into regular shape data, and then uses traditional mature feature extractors to extract voxel features to complete 3D object-detection tasks. VoxelNet [17] uses the above idea to voxelize a point cloud and then uses a 3D CNN to extract the features of sparse voxel mesh to aggregate the spatial context information and obtain the expression of local geometric information of 3D objects. SECOND [12] introduces a sparse convolution method, which improves the speed and efficiency of feature extraction and enables finer voxel granularity when voxelizing point clouds. The voxel R-CNN [10] network fully utilizes voxel features and spatial context information and proposes a fast query technology in the ROI refinement stage to improve the accuracy and speed of 3D object detection. The SE-SSD [18] network adopts the idea of knowledge distillation, introduces the teacher-student model, and uses IoU-based matching strategies to filter soft objects from the teacher network and to encourage the student network to infer complete object shapes. AGO-Net [19] associates the perceptual features from point clouds with more complete features from conceptual models, enhancing the robustness of extracted features, especially for occluded and distant objects. **Pseudo-image-based 3D detection** A pseudo image is obtained by sparsely and irregularly projecting onto a plane. Pseudo images are compact and orderly representations of point clouds, which have at least the following two advantages. First, pseudo images are 2D, and rich features can be extracted using mature, 2D feature extractors or backbone networks. Second, because pseudo images are obtained by point cloud projection, 3D spatial geometric information is retained to a certain extent, which is conducive to accurate position estimation. The PointPillars [9] network divides point cloud data into pillars along the X and Y axis (regardless of the Z axis direction), encodes the characteristics of the midpoint of the pillar and projects them onto the BEV to obtain a pseudo BEV image, and then uses a 2D CNN with high computational efficiency to replace the 3D CNN for end-to-end 3D object detection.