# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 4 Experiments
### Ablation Study  
#### 4.2.1 Depth Fusion Module  
We conducted experiments using various depth fusion strategies, as described in Section 3.3. The results are presented in Table 2. Unfortunately, the outcomes were not optimistic when compared to the baseline BEVDepth [2]. Specifically, the accuracy achieved in monocular 3D object detection did not meet the expected level. This may suggest that the approach proposed in MonoFlex [1] is not particularly effective for the NuScenes dataset. One of the main reasons for this could be the complexity of the method, especially in the global-to-different-image-plane projections, or specifically, six different projections from six cameras. Additionally, the trained model exhibited incorrect depth estimations from the monocular pipeline, which affects the entire framework. The output also revealed issues related to class imbalance. For instance, objects that appear a significant number of times in the NuScenes [3] dataset, such as barriers and traffic cones, showed high mAP values, whereas other object categories performed significantly worse, as shown in Table 3. Consistent with the previous work on MonoFlex [1], which was originally developed and tested on the KITTI [47] benchmark, MonoFlex might perform well on simpler scenes with fewer dynamic objects (such as cars, pedestrians, and cyclists) and less complex environments. MonoFlex may not be optimized for NDS's broader criteria, especially velocity and attribute prediction, which are not emphasized in KITTI, as it focuses on mAP for 3D boxes. Furthermore, augmenting both image-level and BEV-level data increased the overall complexity of the process. Despite these challenges, we proceeded to evaluate the upper performance limit that BEVCorner can achieve.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Configuration** & **mAP(\%) \(\uparrow\)** & **mATE \(\downarrow\)** & **mASE \(\downarrow\)** & **mAOE \(\downarrow\)** & **mAVE \(\downarrow\)** & **mAAE \(\downarrow\)** & **NDS(\%) \(\uparrow\)** \\ \hline \multicolumn{8}{c}{Baseline: BEVDepth [2]} \\ \hline Vanilla: non EMA + non CBGS + 1 key & **33.13** & **0.7009** & **0.2796** & **0.536** & **0.5533** & **0.2273** & **43.59** \\ \hline \multicolumn{8}{c}{BEVCorner (Ours)} \\ \hline direct\_replacement (fixed) & 20.68 & 0.8725 & 0.2945 & 0.6675 & 0.8654 & 0.2516 & 30.83 \\ \hline weighted\_fusion (fixed) & 16.68 & 0.9176 & 0.3451 & 0.8296 & 1.2502 & 0.3679 & 23.74 \\ \hline weighted\_fusion (geometry) & **27.64** & **0.7372** & **0.2938** & **0.5690** & **0.6631** & **0.2465** & **38.72** \\ \hline weighted\_fusion (learned) & 21.87 & 0.8340 & 0.2930 & 0.6437 & 0.8314 & 0.2424 & 32.49 \\ \hline roi\_refinement (fixed) & 20.77 & 0.8652 & 0.2930 & 0.6594 & 0.8249 & 0.2420 & 31.54 \\ \hline roi\_refinement (geometry) & 26.65 & 0.7525 & 0.2930 & 0.5798 & 0.6975 & 0.2560 & 37.53 \\ \hline roi\_refinement (learned) & 22.67 & 0.8247 & 0.2914 & 0.6255 & 0.7853 & 0.2531 & 33.54 \\ \hline hard\_combine (fixed) & 23.32 & 0.7369 & 0.2945 & 0.5780 & 0.7504 & 0.2391 & 35.67 \\ \hline \hline \end{tabular} Note: \(\uparrow\) indicates that higher values are better, while \(\downarrow\) indicates that lower values are better.  
\end{table}
Table 2: Performance of depth fusion methods in BEVCorner.