# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 4 Experiments and Results
### Ablation Experiment  
To validate the effectiveness of each module in the proposed MPVF algorithm, we conducted an ablation experiment on the KITTI validation set. Specifically, we built baseline models based on the PWF and VWF modules, respectively. These baseline models exclude the grouped convolution strategy and the FP module but retain the same detection heads as MPVF. On top of these baselines, we gradually introduced the IR module, FP module, and PVWF module to evaluate their contributions to the overall detection performance, thereby verifying the effectiveness of each component. Table 3 presents the experimental results, where PWF baseline and VWF baseline refer to the two baseline models. 3D AP represents the average precision calculated for the car category at moderate difficulty. In Experiment 1, the detection accuracy of the PWF baseline model was 77.30%. By incorporating the IR module and FP module individually, the accuracy improved to 79.72% and 81.96%, reflecting gains of 2.42% and 4.66%, respectively. When both the IR and FP modules were introduced simultaneously (forming the IRFP module), the accuracy further  
Figure 5: Visual comparison of MPVF and GraphAlign++ in complex scenarios (occlusion, truncation, and distant small objects). The left column shows the results of MPVF, while the right column presents the predictions of GraphAlign++. Red boxes indicate cars and cyclists, while purple boxes represent pedestrians. (**a**) Severe Occlusion Scenario; (**b**) Heavy Truncation Scenario; (**c**) Distant Small Object Scenario. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multirow{2}{*}{
\begin{tabular}{c} **PWF** \\ **Baseline** \\ \end{tabular} } & \multicolumn{2}{c}{**VWF**} & \multicolumn{2}{c}{**IRFP**} & \multirow{2}{*}{**PVWF**} & \multirow{2}{*}{**3D AP (\%)**} \\  & & **Baseline** & & & & & \\ \hline \multirow{4}{*}{1} & \(\surd\) & - & - & - & - & 77.30 \\  & \(\surd\) & - & \(\surd\) & - & - & 79.72 \\  & \(\surd\) & - & - & \(\surd\) & - & 81.96 \\  & \(\surd\) & - & \(\surd\) & \(\surd\) & - & 84.27 \\ \hline \multirow{4}{*}{2} & - & \(\surd\) & - & - & - & 77.12 \\  & - & \(\surd\) & \(\surd\) & - & - & 79.31 \\ \cline{1-1}  & - & \(\surd\) & - & \(\surd\) & - & 81.82 \\ \cline{1-1}  & - & \(\surd\) & \(\surd\) & \(\surd\) & - & 83.94 \\ \hline
3 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 89.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact of individual modules on 3D object detection accuracy. The symbol ”\(\surd\)” indicates that the module is enabled, while “-” denotes that the module is not enabled. increased to 84.27%, resulting in an overall improvement of 6.97%, demonstrating the effectiveness of the IR, FP, and IRFP modules. In Experiment 2, the VWF baseline model achieved an initial accuracy of 77.12%. With the addition of the IR and FP modules, the accuracy increased to 79.31% and 81.82%, respectively, yielding improvements of 2.19% and 4.70%. Incorporating the IRFP module led to a further increase to 83.94%, representing a total gain of 6.82%, again confirming the effectiveness of the IR, FP, and IRFP modules. The results of Experiments 1 and 2 indicate that the IR module enhances feature extraction efficiency, thereby improving detection accuracy. Similarly, the FP module captures fine-grained features across different resolutions, boosting the model's performance in complex scenarios. In Experiment 3, when both the IRFP and PVWF modules were integrated into the PWF and VWF baseline models, the detection accuracy increased to 89.87%. These results highlight the critical role of interactions between local and global features in enhancing detection performance. In summary, each module contributes significantly to the overall performance of MPVF, with the fusion and interaction mechanisms between pointwise and voxelwise features playing a crucial role in achieving optimal detection results. These experimental outcomes further validate the rationality and necessity of the proposed module designs.