# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 2 Related Work  
There are two categories of single-camera 3D object detection methods: pseudo-LIDAR/depth-based methods and methods that do not introduce additional information.  
_Pseudo-LIDAR/Depth-based methods:_ Due to the lack of depth information in monocular vision, the accuracy of these methods is significantly lower compared to methods that incorporate LIDAR and image fusion. Therefore, some pseudo-LIDAR/depth-based methods introduce additional depth estimation modules and/or point cloud assistance [17; 18; 19; 20]. These methods convert image pixels into pseudo-LIDAR point clouds using existing depth estimation techniques, allowing them to leverage LIDAR-based methods for object detection. Ref. [21] introduces a method to generate pseudo-LIDAR data from visual depth estimation, effectively improving the accuracy of 3D object detection in autonomous driving scenarios requiring simulated LIDAR sensors. Mono3D-PLiDAR [22] proposes a novel method that utilizes urban maps to enhance long-range 3D object detection performance. In order to improve the performance of monocular 3D target detection, refs. [23; 24; 25] use the method of converting 2D images to pseudo-LIDAR point clouds. The other method is DA-3 Ddet [26], which adjusts features from the unreliable image-based pseudo-LIDAR domain to the reliable LIDAR domain to improve monocular 3D object detection performance.  
_Methods without introducing additional information:_ M3D-RPN uses an anchor-based approach, utilizing a series of predefined 3D bounding box positions called \"anchors\" and estimating offsets relative to these anchors. One of these methods is M3D-RPN [27], which employs a 3D region proposal network and geometric constraints from both 2D and 3D perspectives to directly regress the 3D position and size of the objects. MonoDLE [28] proposes the use of 3D projected center coordinates to aid in estimating coarse center-aware 3D geometric information. MonoCon [29], an extension of MonoDLE, incorporates a 2D auxiliary learning module for projected objects and introduces Attentive Normalization to all heads. MonoFlex [30] combines the idea of predicting object depth using height ratios with uncertainty theory to construct an ensemble learning approach for estimating the object center position. [PERSON] et al. [31; 32] propose a hybrid approach combining deep learning and geometry to accurately estimate 3D bounding boxes for objects in images. Refs. [33; 34; 35; 36] focus on improving object detection in the context of monocular images. They aim to enhance the accuracy and efficiency of detecting 3D objects using deep learning techniques. RTM-3D [37] and MonoGRNet [14] adopt keypoint-based methods aiming to directly regress keypoints and optimize the estimation of 3D bounding box size and position from the keypoints' image locations.  
However, it is worth noting that existing single-camera 3D detection methods are primarily designed for autonomous driving scenarios and have not been optimized for roadside monitoring scenes. Therefore, this paper proposes the YOLOv7-3D algorithm, which can simultaneously predict the 2D and 3D bounding boxes of traffic objects.