# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 2 Related Works  
Three-dimensional object detection algorithms are the cornerstone of autonomous driving systems, tasked with identifying and precisely localizing road targets such as pedestrians, vehicles, and obstacles from onboard sensor data. Compared to 2D object detection, they provide more precise information, including target categories, locations (center coordinates and dimensions), and poses (rotation angles, etc.), which are crucial for helping autonomous vehicles to make precise decisions in path planning and obstacle avoidance, ensuring the safety and efficiency of driving.  
In recent years, there has been a shift in 3D object detection technology from traditional computer vision methods [14; 15] that rely on manual feature extraction to deep learning-driven techniques. Traditional methods, primarily based on geometric analysis and machine learning techniques such as Support Vector Machines, are limited in their ability to handle complex dynamic scenes due to environmental changes and computational efficiency. In contrast, deep learning methods, through end-to-end feature learning, have achieved single-stage direct prediction, two-stage ROI refinement strategies, and data fusion across mono-modal and cross-modal algorithms, significantly improving detection accuracy and robustness. Some 3D object detection algorithms are listed in Table 1.  
In the domain of single-stage 3D object detection, algorithms such as 3D-SSD [16], IA-SSD [17], VoxelNet [1], SECOND [2], PointPillars [18], CIA-SSD [13], SA-SSD [4], Diswot [19], LargeKernel3D [20], OcTr [21], and VoxelNeXt [22] have achieved rapid and efficient target identification through the optimization of point cloud downsampling, voxelization methods, auxiliary networks, distillation techniques, innovative data structures, and sparse inspection architectures. Two-stage methods like CasA [23], BtcDet [24], MSF [25], and ConQueR [26] have enhanced detection accuracy for occluded and fast-moving targets through fine-grained feature extraction and regression optimization. Cross-modal methods including MV3D [27], EPNet [28], PointPainting [29], MVP [30], LoGoNet [31], SFD [32],and Virtual Conv [33] have further enhanced detection robustness by fusing LiDAR and visual image data.  
LiDAR point cloud technology employs the echo time measurement of laser pulses to generate high-resolution point cloud models, which is vital for precise 3D spatial data acquisition in autonomous driving systems. This methodology surpasses 2D imaging by offering increased resilience against environmental lighting and weather variations, ensuring stable operation in low-visibility conditions. The technology's high-resolution data acquisition and processing capabilities enable autonomous systems to effectively manage occlusions and detect objects across multiple scales, enhancing their perceptual abilities.  
With the continuous advancement of deep learning technology, the integration of LiDAR point cloud and 3D object detection methods has significantly enhanced the accuracy and efficiency of detection in the field of autonomous driving. Methods based on voxels, such as VoxelNet [1], effectively handle large-scale point cloud data through regular three-dimensional voxel grid segmentation and the application of 3D Convolutional Neural Networks (CNNs). Meanwhile, point cloud-based methods, such as PointNet [34] and PointNet++ [35], directly process irregular point clouds and learn their local and global features, ensuring high-precision object detection. Pillar-based methods, such as Point-Pillars [3], enhance real-time detection by converting point cloud data to a pseudo-image plane and combining it with Convolutional Neural Networks. Additionally, the introduction of the Transformer architecture [3], leveraging its powerful attention mechanism for modeling, further captures the global relationships and detailed features within point cloud data, significantly improving the overall performance of 3D object detection.