# Iae-km3D a 3D Object Detection Method Based on an Improved KM3D Network
## 1 Introduction
In 2019, [PERSON] et al. proposed Mono3D ++ [15], which uses a deformable wireframe model to estimate a vehicle's 3D shape and attitude and optimizes the loss of projective consistency between the generated 3D hypotheses and the corresponding 3D pseudo-metrics. The above approach based on the aid of additional data is not applicable in practice because network modelling is done with the help of additional data, which leads to insufficient real-time performance of the modelling algorithms. Methods based on depth information or pseudo-LiDAR point clouds help to improve the accuracy of 3D detection with the help of depth maps or by automatically generating pseudo-LiDAR point clouds from images. For example, Pseudo-LiDAR [16], proposed in 2019, utilizes pre-computed depth maps in combination with the original RGB images to predict a 3D point cloud and then employs a point cloud algorithm for subsequent inference. MonoPSR [17] utilizes ideas such as bounding-box proposal algorithms and shape reconstruction, which first utilizes the fundamental relationships of the pinhole camera model and uses a well-established 2D target detector for the scene. Each target in the scene generates a 3D proposal box, and these proposed 3D positions are shown to be very accurate, which can reduce the difficulty of regressing the final 3D bounding box. Meanwhile, MonoPSR predicts the point cloud in a coordinate system centered on the target and enhances the accuracy of 3D localization by learning local size and shape information. Multi-task network-based AM3D [18] converts a 2D image into a 3D point cloud plane by combining it with a depth map and then uses PointNet [19] to estimate the 3D dimensions, positions and orientations. In 2020, ForeSeE [20], proposed by [PERSON] separates the foreground and background portions of an image, and then, using monocular vision, separates depth estimation and, finally, achieves feature enhancement using the depth estimate of the foreground. The above methods require depth prediction or pseudo-LiDAR point cloud generation before 3D target detection, which increases the overall computational overhead of the model. Although high accuracy can be achieved, real-time performance cannot be guaranteed. Methods relying solely on RGB images are more streamlined than methods introducing additional data or network models. [PERSON] et al. proposed Deep3 DBox in 2017 [21], which estimates the local orientation of each object and the 2D and 3D bounding box constraint relationships by introducing a bin-based discretization method to obtain a complete 3D pose. Deep3 DBox adds a pose regression network constructed based on a fully connected layer behind an arbitrary 2D target detector. It completes the spatial coordinate prediction using a target 3D centroid solver module. The effectiveness of Deep3 dBox depends heavily on the performance of the dependent 2D detectors, such as the Shift R-CNN proposed by [PERSON] et al. [22] and the FQNet [23] proposed by [PERSON] et al., which add fine-tuning of the first-stage pose regression results to Deep3 DBox, increasing the accuracy of 3D pose prediction. MonoDIS [24], proposed by [PERSON] et al. in 2019, employs a two-stage architecture for monocular 3D detection. The algorithm is designed for the training process to simultaneously regress target center, size, and orientation, which brings about the problem of different loss sizes in each part; therefore, a decoupled regression loss is designed, which divides the regression part into several groups; each group only has its parameters to be learned, and the other parts are replaced using labels, which makes the training more stable. MonoGRNet [25], proposed by [PERSON] et al., subdivided the 3D object localization task into four tasks--two-dimensional detection, instance depth estimation, 3D object position estimation, and local corner point estimation--and then stacked these components together to refine the 3D bounding box in the global context information.