# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 4 Experiments and Analysis
### Experiment Analysis
#### 4.4.2 Representative Cases  
Due to PointPillars' exceptional performance, ranking second among the evaluated algorithms, this section primarily presents a visual comparison between MS3D (our model) and PointPillars. Figures 5a, 6a and 7a represent the point cloud data labels. 1. Easy Category: Minor Occlusion of Small Objects  
In scenarios with simple categories, detecting small objects is challenging due to slight occlusions and sparse point cloud data, as shown in Figure 5. Sparse datasets lead to insufficient point density for small or distant targets, complicating detection. The pronounced data voids within sparse datasets lead to a marked reduction in informative content, thereby complicating the extraction of meaningful patterns during the learning process. This difficulty is particularly acute for small or remotely positioned targets, which are intrinsically under-represented in the dataset. The sparsity of the data further compounds the scarcity of feature information critical for detection, thereby intensifying the challenges in achieving precise identification and classification of these objects. Figure 5b demonstrates that traditional methods like PointPillars struggle with missed detections, such as failing to identify a distant small vehicle obscured by closer ones, highlighting limitations in handling occlusions and multi-scale detection. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Frame Rate (HZ)**} & \multicolumn{3}{c}{**AP**} & \multicolumn{3}{c}{**F1 Score**} \\ \cline{3-8}  & & **BBOX** & **BEV** & **3D** & **BBOX** & **BEV** & **3D** \\ \hline VoxelNet & 4.4 & 0.63 & 0.74 & 0.73 & 0.45 & 0.47 & 0.49 \\ SECOND & 20 & 0.74 & 0.78 & 0.80 & 0.49 & 0.52 & 0.53 \\ PointPillars & 62 & 0.75 & 0.84 & 0.83 & 0.52 & 0.54 & 0.53 \\ MS3D & 60 & 0.77 & 0.89 & 0.88 & 0.53 & 0.55 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A performance comparison of MS3D with other methods in the difficult category. MS3D mitigates these issues by employing advanced feature fusion techniques, combining upsampling and downsampling to integrate multi-scale features effectively. This strategy enhances the model's ability to focus on critical features and reduces the impacts of less relevant ones, improving detection performance for small objects. Additionally, 2D convolutions enhance the model's ability to interpret 2D projections of point cloud data, enabling more accurate detection of small targets under slight occlusions. As illustrated in Figure 5c, MS3D's feature fusion approach yields higher detection confidence, accurately identifying occluded distant small vehicles and significantly improving small object detection. 2. Moderate Category: Overlapping and Occluded Multi-Object  
In moderate-category scenarios, multi-target overlap and occlusion pose significant challenges due to the unstructured nature of point cloud data, which complicates feature extraction, as shown in Figure 6. For instance, at an intersection, multiple vehicles overlap, with one black car nearly obscured on its left side. As depicted in Figure 6b, traditional methods like PointPillars fail to detect this occluded black car, resulting in detection failures. In contrast, MS3D, leveraging the SecondFPN, effectively detects the occluded black car. The SecondFPN's multi-scale feature extraction and fusion mechanisms enable the  
Figure 5: Comparison of detection performance for small objects with minor occlusion. precise identification of targets across various scales, enhancing detection performance in complex occlusion scenarios. Additionally, the Adam optimizer's adaptive learning rate facilitates handling the complexities of such scenes. As shown in Figure 6c, MS3D's feature fusion capabilities significantly improve detection accuracy, effectively mitigating interference from overlapping targets. 3.