# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 3 BEVCorner  
### Baseline and Motivation  
BEVCorner is the proposed method designed to improve 3D object detection accuracy in autonomous driving scenarios by synergistically combining the strengths of both monocular and multi-view camera systems. It aims to fuse the outputs from these two systems through depth map enhancement, providing a novel approach to address the challenges of depth estimation and scene understanding in BEV space. This method is particularly motivated by the need to leverage the complementary information from single-camera (monocular) and multiple-camera (multi-view) setups to achieve more robust and accurate detection (see Table 1), especially in complex urban environments. To establish a foundation for BEVCorner, two state-of-the-art methods are selected as baselines: BEVDepth [2] for multi-view depth estimation and MonoFlex [1] for monocular 3D object detection. The following paragraphs provide the details of each method, their capabilities, and their limitations. BEVDepth [2] has demonstrated superior performance over several baseline methods on the NuScenes [3] dataset. It is a state-of-the-art method for depth estimation from multi-view images in autonomous driving. For instance, it achieves competitive results in terms of depth estimation accuracy, with metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). It leverages the Bird's-Eye View (BEV) structure to self-supervise depth learning from images, eliminating the need for explicit depth labels from LiDAR. This self-supervision is achieved by exploiting the inherent relationship between image pixels and their corresponding positions in the BEV, which naturally encodes depth information. However, BEVDepth may struggle with depth estimation for distant or occluded objects due to the limitations of image-based depth estimation. The self-supervision relies on the BEV structure, which can be less effective in scenarios with sparse visual cues or complex occlusions, potentially leading to inaccuracies in long-range depth predictions. MonoFlex [1] excels in monocular 3D object detection, particularly on the KITTI [47] dataset, where it achieves high mean Average Precision (mAP) for 3D object detection, demonstrating its effectiveness in leveraging rich visual cues and contextual information from individual camera views. It is a flexible single-stage monocular 3D object detection framework capable of handling different object categories effectively without category-specific architectures. It is extended from CenterNet [12] and incorporates a size-adaptive module and a depth refinement module to estimate 3D bounding boxes from single images. The size-adaptive module adjusts to varying object sizes, while the depth refinement module enhances depth estimation by leveraging mathematical priors and uncertaintymodeling. As a monocular method, MonoFlex faces ambiguity in depth estimation from a single view, which can lead to inaccuracies in 3D positioning, especially for objects at varying distances or in occluded scenarios. This depth ambiguity is a known challenge in monocular vision, limiting its robustness compared to multi-view approaches. The motivation for BEVCorner stems from the recognition that both methods, while individually strong, have complementary strengths and limitations that can be addressed through their integration. The primary goal is to enhance 3D object detection accuracy by leveraging accurate depth estimation from BEVDepth [2] and rich contextual information from MonoFlex [1]. By combining these two approaches, BEVCorner aims to enhance depth estimation by integrating the multi-view depth estimates and monocular depth refinement. It also improves scene understanding by utilizing detailed object recognition capabilities and spatial object-centric depth. This fusion is expected to provide a more robust and accurate solution for 3D object detection, particularly in urban driving scenarios where both depth accuracy and object context are critical.