# VirtualFilter: A High-Performance Multimodal 3D Object Detection Method with Semantic Filtering
## 4 Experiments and Results
### Ablation Study  
We performed comprehensive ablation studies on the VirtualFilter model by evaluating the impact of different discard rates and learning rates. Fine-tuning these parameters allowed our model to perform optimally for specific datasets. It is important to note that we kept other parameters consistent while testing different settings for the modules being investigated. Additionally, the effectiveness of each module is further validated through quantitative experiments. **Effect of semantic filter. ** We first incorporated the semantic filtering module into VirConvNet to assess the effectiveness of the semantic filtering strategy. Additionally, we combined the semantically filtered virtual point set with the original point cloud set and train it on the Voxel-RCNN network, which uses only LiDAR data as input. The results of both approaches are presented in Table 3. As shown in the table, using semantically filtered virtual points as input enhances the accuracy of 3D car detection. Furthermore, this virtual point set can be integrated with any method that uses only point cloud data, improving detection performance by merging it with the original point cloud. **Effect of 3D-DGAF. ** We used 3D-DGAF for VirconvNet to conduct experiments to verify the effectiveness of the module. We trained both networks equally for 60 epochs on \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Modality**} & \multicolumn{4}{c}{**Car 3D AP R40**} & \multicolumn{4}{c}{**Car BEV AP R40**} \\ \cline{3-8}  & & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline PointPillars [4] & LiDAR & 82.58 & 74.31 & 68.99 & 90.07 & 86.56 & 82.81 \\ SA-SSD [26] & LiDAR & 88.75 & 79.79 & 74.16 & 95.03 & 91.03 & 85.96 \\ PV-RCNN [11] & LiDAR & 90.25 & 81.43 & 76.82 & 94.98 & 90.65 & 86.14 \\ Voxel-RCNN [12] & LiDAR & 90.90 & 81.62 & 77.06 & 94.85 & 88.83 & 86.13 \\ CasA [27] & LiDAR & 91.58 & 83.06 & 80.08 & 95.19 & 91.54 & 86.62 \\ \hline MV3D [8] & LiDAR + RGB & 74.97 & 63.63 & 54.00 & 86.62 & 78.93 & 69.80 \\
3D-CVF [28] & LiDAR + RGB & 89.20 & 80.05 & 73.11 & 93.52 & 89.56 & 82.45 \\ VPFNet [29] & LiDAR + RGB & 91.02 & 83.21 & 78.20 & 93.94 & 90.52 & 86.25 \\ SFD [6] & LiDAR + RGB & 91.73 & 84.76 & 77.92 & 95.64 & 91.85 & 86.83 \\ VirConv-T [7] & LiDAR + RGB & **92.54** & 86.25 & 81.24 & 96.11 & 92.65 & **89.69** \\ \hline
**Ours** & LiDAR + RGB & 92.45 & **86.28** & **82.21** & **96.15** & **92.71** & 89.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the state-of-the-art approaches for car 3D object detection on the KITTI test dataset. It can be seen that our model outperforms other networks in both 3D and BEV evaluation metrics. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Modality**} & \multicolumn{4}{c}{**Car 3D AP R40**} \\ \cline{3-5}  & & **Easy** & **Mod.** & **Hard** \\ \hline PV-RCNN & LiDAR & 92.57 & 84.83 & 82.69 \\ Voxel-RCNN & LiDAR & 92.38 & 85.29 & 82.86 \\ CasA & LiDAR & 93.38 & 86.42 & 84.04 \\
3D-CVF & LiDAR + RGB & 89.67 & 79.88 & 78.47 \\ SFD & LiDAR + RGB & 95.47 & 88.56 & 85.74 \\ VirConv-T & LiDAR + RGB & 95.59 & 88.12 & 85.73 \\ \hline
**Ours** & LiDAR + RGB & **95.61** & **88.15** & **87.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the state-of-the-art approaches for car 3D detection on the KITTI val set. It can be seen that our model outperforms other networks in 3D evaluation metrics. conducted on the KITTI dataset, which, despite being widely used, may not comprehensively represent all traffic scenarios. The dataset's scale and diversity are limited, which could impact the generalizability of the results. To address these limitations, future research could focus on the following directions:  
1. Expanding the scope of experiments to include larger and more diverse datasets to improve the model's generalization and applicability;
2.