# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 4 Experiments and Results
### Experimental Results
#### 4.4.1 Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Running**} & \multirow{2}{*}{**Sensor**} & \multicolumn{4}{c}{**Car 3D AP (\%)**} & \multicolumn{4}{c}{**Pedestrians 3D AP (\%)**} & \multicolumn{4}{c}{**Cyclists 3D AP (\%)**} \\  & & & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline STD [55] & 2019 & 0.08 & & 87.95 & 79.71 & 75.09 & 53.29 & 42.47 & 38.35 & 78.69 & 61.59 & 55.30 \\ Part-A2 Net [56] & 2020 & 0.08 & & 87.81 & 78.49 & 73.51 & 53.10 & 43.35 & 40.06 & 79.71 & 63.52 & 56.93 \\ Point-CNN [24] & 2020 & 0.60 & & 88.33 & 79.47 & 72.29 & 51.92 & 43.77 & 40.14 & 78.60 & 63.48 & 57.08 \\
3 DSSD [27] & 2020 & 0.04 & & 88.36 & 79.57 & 74.55 & 54.64 & 44.27 & 40.23 & 82.48 & 64.10 & 56.90 \\ SA-SSD [57] & 2020 & 0.04 & & 88.75 & 79.79 & 74.16 & - & - & - & - & - \\ PV-KCNN [58] & 2020 & 0.08 & & 90.25 & 81.43 & 76.82 & 52.17 & 43.29 & 40.29 & 78.60 & 63.71 & 57.65 \\ Pyramid R-CNN [59] & 2021 & 0.13 & LIDAR & 88.39 & 82.08 & 77.49 & - & - & - & - & - \\ CIA-SSD [28] & 2021 & 0.03 & & 89.59 & 80.28 & 72.87 & - & - & - & - & - \\ Voxel R-CNN [7] & 2021 & 0.04 & & 90.90 & 81.62 & 77.06 & - & - & - & - & - \\ M3 DERR [60] & 2022 & - & & 90.28 & 81.73 & 76.96 & 45.70 & 39.94 & 37.66 & 83.83 & 66.74 & 59.03 \\ FAPNet-Net [61] & 2023 & 0.06 & & 88.36 & 81.53 & 78.98 & - & - & - & - & - \\ PVT-SSD [8] & 2023 & 0.05 & & 90.65 & 82.29 & 76.85 & - & - & - & - & - & - \\ APVR [44] & 2023 & **0.02** & & 91.45 & 82.17 & 78.08 & 55.76 & 44.87 & 41.34 & - & - & - \\ GLENet-VR [45] & 2023 & 0.04 & & 91.67 & 83.23 & 78.43 & - & - & - & - & - \\ \hline MV3D [30] & 2017 & 0.36 & & 74.97 & 63.63 & 54.00 & - & - & - & - & - & - \\ AVOD [31] & 2018 & 0.08 & & 76.39 & 66.47 & 60.23 & 36.10 & 27.86 & 25.76 & 57.19 & 42.08 & 38.29 \\ F-PointNet [10] & 2018 & 0.17 & & 82.19 & 69.79 & 60.59 & 50.53 & 42.15 & 38.08 & 72.27 & 56.12 & 49.01 \\ Confuse [32] & 2018 & 0.06 & & 83.68 & 68.78 & 61.67 & - & - & - & - & - \\ MVX-Net [14] & 2019 & - & & 83.20 & 72.70 & 65.20 & - & - & - & - & - & - \\ F-ConvNet [29] & 0.47 & & 87.36 & 76.39 & 66.69 & 52.16 & 43.38 & 38.80 & 81.98 & 65.07 & 56.54 \\ PointPainting [9] & 2020 & 0.40 & & 82.11 & 71.70 & 67.08 & 50.32 & 40.97 & 37.87 & 77.63 & 63.78 & 55.89 \\ CLOCs [12] & 2020 & 0.15 & & 88.94 & 80.67 & 77.15 & - & - & - & - & - \\ F-PointPillars [62] & 2021 & 0.07 & Fusion & & - & 51.22 & 42.89 & 39.28 & - & - & - \\ Fast-CLOCs [13] & 2022 & 0.13 & & 89.11 & 80.34 & 76.98 & 52.10 & 42.72 & 39.08 & 82.83 & 65.31 & 57.43 \\ EPNet++ [63] & 2022 & 0.10 & & 91.37 & 81.96 & 76.71 & 52.97 & 44.38 & 41.29 & 76.15 & 59.71 & 53.67 \\ PA3 DNet [64] & 2023 & 0.10 & & 90.49 & 82.57 & 77.88 & - & - & - & - & - \\ ACF-Net [46] & 2023 & 0.11 & & 90.80 & 84.67 & 80.14 & 54.62 & 46.36 & 42.57 & 84.29 & 68.37 & 62.08 \\ VirConv-L [47] & 2023 & 0.06 & & 91.41 & 85.05 & 80.22 & - & - & - & - & - \\ GraphAlign++ [48] & 2024 & 0.15 & & 90.98 & 83.76 & 80.16 & 53.31 & 47.51 & 42.26 & 79.58 & 65.21 & 55.68 \\ SQL [49] & 2024 & 0.06 & & 91.58 & 81.82 & 79.07 & - & - & - & - & - \\ RoboFusion-L [50] & 2024 & 0.30 & & 91.75 & 84.08 & **90.71** & - & - & - & - & - \\ MPVF(Ours) & 2024 & 0.33 & & **91.93** & **85.12** & 79.14 & **56.10** & **48.61** & **43.96** & **85.47** & **70.12** & **62.69** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of 3D object detection performance for cars on the KITTI validation set. The best and second-best results are highlighted in bold and underlined, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Type**} & \multicolumn{4}{c}{**Car 3D AP (\%)**} & \multicolumn{4}{c}{**Car 3D AP (\%)**} \\  & & & **Easy** & **Mod. ** & **Hard** & **Easy** & **Mod. ** & **Hard** \\ \hline SMOKE [4] & 2020 & & & 14.76 & 12.85 & 11.50 \\ DSGN [51] & 2020 & & 72.31 & 54.27 & 47.71 \\ M