# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 4 Experiments and Results
### Experimental Results
#### 4.4.1 Quantitative Analysis  
1. Evaluation on the KITTI Test Set  
We conducted a quantitative comparison of the proposed MPVF method against previous state-of-the-art 3D object detection methods on the KITTI benchmark dataset, as shown in Table 1. Although our model exhibited slightly longer running times, likely due to its higher complexity, the overall detection accuracy surpassed that of advanced unimodal methods such as LiGA-Stereo [42], StereoDistill [43], PVT-SSD [8], APVR [44], and GLENet-VR [45]. Notably, our method demonstrated significant advantages in detecting pedestrians and cyclists. This indicates that MPVF effectively integrates multimodal information and outperforms traditional unimodal detection methods in scenarios involving small targets and complex environments, showcasing the efficacy and practicality of our approach. When compared to advanced multimodal fusion methods such as ACF-Net [46], VirConv-L [47], GraphAlign++ [48], SQD [49], and RoboFusion-L [50], our method also exhibited superior performance, achieving 3D AP of 85.12%, 48.61%, and 70.12% for medium-difficulty detections of cars, pedestrians, and cyclists, respectively. It should be noted that in pedestrian detection tasks, the complexity of pedestrian actions and pose variations usually result in lower 3D detection accuracy, as is also reflected in existing research. Therefore, although the inference time is slightly longer, our method is still shown to outperform most existing state-of-the-art techniques, especially in complex scenarios. The mAP for the three categories of MPVF was 69.24%, representing an improvement of 2.75% over the mAP of 66.49% achieved by GraphAlign++ [48], further validating the effectiveness of our method. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Running**} & \multicolumn{2}{c}{**Car 3D AP (\%)**} & \multicolumn{2}{c}{**Pedestrians 3D AP (\%)**} & \multicolumn{2}{c}{**Cyclists 3D AP (\%)**} \\  & & **Time(s)** & & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline SMOKE [4] & 2020 & 0.03 & & 14.03 & 9.76 & 7.84 & - & - & - & - & - & - \\ DSGN [51] & 2020 & 0.67 & & 73.50 & 52.18 & 45.14 & 20.53 & 15.55 & 14.15 & 27.76 & 18.17 & 16.21 \\ M3 DSSD [17] & 2021 & - & 17.51 & 11.46 & 8.98 & 5.16 & 3.87 & 3.08 & 2.10 & 1.51 & 1.58 \\ MonoRUn [18] & 2021 & 0.07 & & 19.65 & 12.30 & 15.08 & 10.88 & 6.78 & 5.83 & 1.01 & 0.61 & 0.48 \\ MonoFlex [19] & 2021 & 0.03 & & 19.94 & 13.89 & 12.07 & 9.43 & 6.31 & 5.26 & 4.17 & 2.35 & 2.04 \\ LiGA-Stereo [42] & 2021 & 0.40 & & 81.39 & 64.66 & 57.22 & 40.46 & 30.00 & 27.07 & 54.44 & 36.86 & 32.06 \\ ImVoxelNet [5] & 2022 & 0.20 & & 17.15 & 10.97 & 9.15 & - & - & - & - & - \\ MonODTER [52] & 2023 & 0.04 & & 25.00 & 16.47 & 13.58 & - & - & - & - & - & - \\ StereoDistill [43] & 2023 & 0.40 & & 81.66 & 66.39 & 57.39 & 44.12 & 32.23 & 28.95 & 63.96 & 44.02 & 39.19 \\ MonoCD [53] & 2024 & 0.04 & & 25.53 & 16.59 & 14.53 & - & - & - & - & - & - \\ \hline VoxelNet [33] & 2018 & 0.22 & & 77.47 & 65.11 & 57.73 & 39.48 & 33.69 & 31.51 & 61.22 & 48.36 & 44.37 \\ SECOND [54] & 2018 & 0.05 & & 83.13 & 73.66 & 66.20 & 51.07 & 42.56 & 37.29 & 70.51 & 53.85 & 46.90 \\ PointRCNN [23] & 2019 & 0.10 & & 86.96 & 75.64 & 70.70 & 47.98 & 39.37 & 36.01 & 74.96 & 58.82 & 52.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of 3D object detection performance for cars, pedestrians, and cyclists on the KITTI test set. The best and second-best results are highlighted in bold and underlined, respectively. A “-” indicates that the data are not publicly available.