# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 1 Introduction  
In an autonomous driving system, 3D vehicle detection is essential to ensure safety, which can provide the reliable relative location and speed information between the vehicle and other traffic participants for drivers or an intelligent control system to avoid collisions. Unlike classical 2D object detection methods [1; 2] that obtain objects' categories and bounding boxes in the image plane, 3D object detection methods can reveal more detailed information about the objects, including the physical size, relative position, and heading information. This information is crucial for intelligent driving tasks, such as behavior decision, path planning, navigation control, collision avoidance, etc. However, even with this information, a lower performance is achieved compared with that of 2D object methods. On the KITTI dataset, the average precision (AP) of 2D vehicle detectors is about 15% higher than that of 3D detectors [3]. Therefore, 3D object detection methods need to be further studied. According to the sensors most commonly used in the traffic scene, 3D object detection methods can be roughly divided into three categories [4]: image-based, point-cloud-based, and fusion-based methods. Image data can provide abundant texture properties, which are of great significance to object recognition. However, the lack of explicit depth information restricts the accuracy of position and bounding box regression for objects, especially long-range small-scale objects. Point cloud data canretain the physical size and depth information of the object, which is beneficial to position and 3D bounding box regression. However, due to its sparsity and lack of texture information, many kinds of objects have similar feature representations in 3D point cloud space, which is not conducive to object recognition. Fusion-based methods can make full use of the advantages of the two sensors and achieve a better detection performance. Influenced by the rapid development of 2D object detection methods, a LiDAR(Light Detection and Ranging)point cloud is usually projected as pseudo images, such as a FV (front view) map [5] or a bird's-eye view (BEV) map [6; 7; 8], and then the pseudo images and RGB images are fed to the two-stage 2D detector with an extension to perform 3D bounding box regression, classification, and orientation estimation. The fusion-based methods have achieved good performance, but there are still some shortcomings to be solved. First, projecting the LiDAR point cloud as a BEV map creates a loss of a large amount of information, especially the height information, causing a significant gap between AP\({}_{\text{BEV}}\) and AP\({}_{\text{3D}}\). With the KITTI dataset, the AP\({}_{\text{BEV}}\) is more than 10% higher than AP\({}_{\text{3D}}\). Being able to retain as much information as possible on the BEV map will directly affect the final detection results. Second, the procedure of the two-stage detection structure is complex, the utilization efficiency of feature is low, and the parameters are redundant, causing a low generalization capability of the network. For example, the backbone network usually uses a VGG16-like [9] (visual geometry group) structure in the feature extractor; the refinement network adopts cascaded, fully connected layers (FC) [6; 7], which lead to a huge number of parameters and a lower performance compared with other methods. A standard VGG16 network has more than 138 million parameters; some literature has proved that most of them have little effect on the final results [10; 11]. ResNet [10] introduces a residual structure with only 25.5 million parameters, but the classification performance is higher than that of VGG16 with the ImageNet dataset. Deep Compression [11] achieves about 40 times the compression rate based on VGG16 networks by pruning, trained quantization, and Huffman coding, and the performance of the network has risen rather than fallen. In addition, the design of a loss function for 3D object detection is more complex than that of 2D object detection.