# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 3 BEVCorner
### Baseline and Motivation
One of the primary challenges in combining BEVDepth and MonoFlex is aligning the image features from the monocular and multi-view pipelines to achieve collaborative perception. Initially, it might seem beneficial to use a shared backbone for feature extraction to ensure consistency in feature representation across both pipelines. Multi-view BEV pipeline requires features that are contextual across multiple views in both spatial and temporal dimensions to improve the accuracy of Inverse Perspective Mapping (IPM). Monocular pipelines benefit from features that are rich in 2D contextual information from individual images, such as textures, object appearance, and semantic context, which are crucial for object recognition and localization in single views. These features are optimized for monocular depth estimation and 3D bounding box prediction, leveraging the size-adaptive \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspects** & **BEV Object Detection** & **Monocular Object Detection** \\ \hline Input Data & Typically uses LiDAR point clouds or fused multi-sensor data (e.g., LiDAR + camera). & Uses a single RGB camera image. \\ \hline Depth Information & Directly available from LiDAR or depth sensors, providing accurate 3D spatial information. & Inferred from 2D images using monocular depth estimation, which may be less accurate. \\ \hline Strengths & \(\checkmark\) Precise depth and spatial data improve object positioning in 3D space. Objects appear at consistent scales in BEV maps. & \(\checkmark\) Rich Semantic Information, e.g., detailed visual features (e.g., texture, color) to enhance classification. \\ \cline{2-3} Weakness & - LiDAR data can miss distant or small objects due to sparsity. & - Struggles with accurate 3D localization due to inferred depth. \\ \cline{2-3}  & - Processing point clouds or BEV maps requires significant resources. & - Occluded objects in the image plane are hard to detect. \\ \cline{2-3}  & & - Objects at different distances vary in size, complicating detection. \\ \cline{2-3}  & & - Performance drops in poor conditions (e.g., rain, darkness). \\ \hline Typical Applications & Autonomous driving (e.g., obstacle detection, path planning), robotics (e.g., navigation in structured environments). & Surveillance and security, consumer electronics (e.g., smartphones, AR/VR), and low-cost autonomous systems. \\ \hline Computational Cost & High, due to multi-cameras and processing large point clouds or multi-sensor fusion. & Lower, though depth estimation can in-crease complexity. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of BEV and monocular object detection and depth refinement modules. Therefore, BEVCorner employs separate backbones for feature extraction in the multi-view BEV and monocular detection pipelines, extracting different types of image features tailored to each task's requirements. The fusion process then involves enhancing the depth map using the multi-view BEV representation from BEVDepth and integrating it with the object detection outputs from MonoFlex, potentially through a depth map enhancement module, to achieve improved detection accuracy. BEVCorner represents a promising approach to enhancing 3D object detection as it aims to improve detection accuracy and robustness, setting the stage for more reliable autonomous driving systems.