# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 4 Experiments and Results
### Experimental Results
#### 4.4.2 Qualitative Analysis  
We conducted inference tests on the KITTI validation set using MPVF and GraphAlign++ [48] and visualized their 3D detection results in specific scenarios to highlight MPVF's performance advantages and demonstrate the applicability of the proposed network structure. Figures 4 and 5 show the 3D object detection results for cars, pedestrians, and cyclists under various road types and complex environments using both methods. Figure 4 presents the detection results across highways, intersections, suburban roads, and rural roads: (a) Highway: MPVF outperforms GraphAlign++ in detecting distant vehicles. The green circles highlight missed or incorrectly detected vehicles by GraphAlign++, while MPVF accurately identifies them; (b) Intersection: MPVF correctly detects all vehicles, while GraphAlign++ incorrectly identifies a road sign as a pedestrian, demonstrating the superior robustness of MPVF; (c) Suburban Road: GraphAlign++ misclassifies a cyclist as a pedestrian and misses distant vehicles, whereas MPVF effectively detects both, showcasing its advantage in long-range small-object detection; (d) Rural Road: GraphAlign++ misclassifies a pedestrian as a cyclist, while MPVF exhibits higher accuracy in detecting distant pedestrians. Figure 5 illustrates the detection results in challenging scenarios with severe occlusion, heavy truncation, and distant small objects: (a) Severe Occlusion: GraphAlign++ fails to detect two cars; (b) Heavy Truncation: GraphAlign++ misses both a truncated \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Year** & **Type** & \multicolumn{3}{c}{**Car 3D AP (\%)**} \\  & & **Easy** & **Moderate** & **Hard** \\ \hline VoxelNet [33] & 2018 & & 81.97 & 65.46 & 62.85 \\ SECOND [54] & 2018 & & 87.43 & 76.48 & 69.10 \\ PointRCNN [23] & 2019 & & 88.88 & 78.63 & 77.38 \\ STD [55] & 2019 & & 89.70 & 79.80 & 79.30 \\ Part-A2 Net [56] & 2020 & & 89.47 & 79.47 & 78.54 \\ Point-GNN [24] & 2020 & & 87.89 & 78.34 & 77.38 \\
3 DSSD [27] & 2020 & & 89.71 & 79.45 & 78.67 \\ SA-SSD [57] & 2020 & LIDAR & 92.23 & 84.30 & 81.36 \\ PV-RCNN [58] & 2020 & & 92.57 & 84.83 & 82.69 \\ CIA-SSD [28] & 2021 & & 93.59 & 84.16 & 81.20 \\ Voxel R-CNN [7] & 2021 & & 92.38 & 85.29 & 82.86 \\ M3 DETR [60] & 2022 & & 92.29 & 85.41 & 82.25 \\ FARP-Net [61] & 2023 & & 89.91 & 83.99 & 79.21 \\ APVR [44] & 2023 & & 93.49 & 85.62 & 81.14 \\ GLENet-VR [45] & 2023 & & 93.51 & 86.10 & 83.60 \\ \hline MV3D [30] & 2017 & & 71.29 & 62.68 & 56.56 \\ AVOD [31] & 2018 & & 84.41 & 74.44 & 68.65 \\ F-PointNet [10] & 2018 & & 83.76 & 70.92 & 63.65 \\ ContFuse [32] & 2018 & & 86.32 & 73.25 & 67.81 \\ MVX-Net [14] & 2019 & & 85.50 & 73.30 & 67.40 \\ F-ConvNet [29] & 2019 & & 89.02 & 78.80 & 77.09 \\ CLOCs [12] & 2020 & & 92.48 & 82.79 & 77.71 \\ F-PointPillars [62] & 2021 & Fusion & 88.90 & 79.28 & 78.07 \\ EPNet++ [63] & 2022 & & 92.51 & 83.17 & 82.27 \\ PA3 DNet [64] & 2023 & & 93.18 & 86.02 & 83.74 \\ ACF-Net [46] & 2023 & & 93.17 & 88.80 & **86.53** \\ VirConv-L [47] & 2023 & & 93.36 & 88.71 & 85.83 \\ GraphAlign++ [48] & 2024 & & 92.58 & 87.01 & 84.68 \\ RoboFusion-L [50] & 2024 & & 93.30 & 88.04 & 85.27 \\ MPVF(Ours) & 2024 & & **93.68** & **89.87** & 85.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._vehicle and three distant cars; (c) Distant Small Objects: GraphAlign++ fails to detect remote cyclists and vehicles. In contrast, MPVF accurately detects objects under all these challenging conditions. These comparative results highlight MPVF's significant advantage in detecting distant small objects, validating the effectiveness of its local and global feature fusion strategy. By enhancing interactions between local and global features, this strategy significantly improves detection accuracy, particularly for small objects. The results demonstrate MPVF's technical superiority in various complex environments. Figure 4: Cont. Figure 4: Visual comparison of MPVF and GraphAlign++ across different road types.