# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 5 Conclusions and Future Work  
A novel framework, BEVCorner, was presented in this research work for enhancing BEV object detection by integrating monocular features via a depth fusion module. The idea is rooted in the observation that, while multi-view systems like BEVDepth [2] provide dense BEV representations, they lack fine-grained object details, and monocular approaches such as MonoFlex [1] struggle with depth ambiguity. The strengths of both BEV and monocular approaches were then complemented by using depth fusion modules. Four fusion techniques--direct replacement, weighted fusion, region-of-interest refinement, and hard combine--were explored to balance the strengths of monocular and BEV depth estimation. Through extensive experiments on the NuScenes dataset [3], the potential of BEVCorner was demonstrated. The baseline result of 38.72% NDS was obtained, lagging behind the BEVDepth baseline [2] of 43.59% due to challenges in aligning the monocular pipeline, specifically MonoFlex [1]. Further insights into this observation are provided in the ablation study presented in Section 4.2. Despite this limitation, the potential of the approach is considered promising. To evaluate the upper bound performance, experiments were conducted in which the network was trained using object-centric ground truth depth. A significant improvement was yielded, with an NDS score of 53.21% being achieved, representing a 9.62% increase without new modalities or excessive computational resources being introduced. An increase in the number of parameters from 76.4 M to 97.9 M was observed, reflecting a 21.96% rise in trainable parameters. This trade-off, where a 9.62% improvement in NDS is obtained, demonstrates the value of the approach. Two potential enhancements could further improve the performance of BEVCorner. First, multi-camera fusion could be implemented to address limitations in monocular object detection methods, which rely solely on 2D images from a single camera view. Data from six cameras are provided by the NuScenes dataset [3], offering a more comprehensive field of view. In future work, transformers [54] could be leveraged to fuse features from all camera views before they are passed to the detection head, potentially improving coverage and accuracy. Second, alternative representations for detected objects could be explored. In this experiment, axis-aligned bounding boxes were used to represent all detected objects, as shown in Figure 2. While this approach is straightforward, rotation information cannot be captured, as the bounding boxes remain parallel to the image axes. In reality, slight rotations may be exhibited by many detected objects, making axis-aligned boxes suboptimal. Therefore, oriented bounding boxes or circular regions could be investigated in future work to better capture the spatial orientation of objects. Conceptualization, J.N. and L.L. ; methodology, J.N. ; validation, J.N. ; formal analysis, Q.L. ; investigation, J.N. and Z.L. ; resources, J.N. ; data curation, J.N. ; writing--original draft preparation, J.N. and L.L. ; writing--review and editing, J.N., Q.L., and Y.G. ; visualization, J.N. ; supervision, Z.L., L.L., and Y.G. ; project administration, Y.G. ; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **\# Params** & **\# Eval Time** &
\begin{tabular}{c} **\# Eval Max GPU** \\ **Memory Usage** \\ \end{tabular} \\ \hline BEVDepth [2] & 76.4 M & 115 s & 1311.20 MB \\ MonoFlex [1] as & 21.5 M & - & - \\ monocular pipeline & 97.9 M & 135 s & 2447.44 MB \\ BEVCorner (ours) & +21.96\% & +20 s & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Resource usage from BEVCorner. **Funding:** This work was supported by the Shenzhen Science and Technology Innovation Committee under Grant JSGG20211029100204006. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.