Autonomous driving paper index
A review of multi-sensor fusion 3D object detection for autonomous driving
One-line summary
In this paper, we provide a review of 3D object detection methods for multi-sensor fusion.
Engineering notes
Key topics: autonomous driving system, autonomous driving, 3d object detection, object detection, lidar, sensor fusion, multi-sensor fusion. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
3D object detection is an important part of autonomous driving systems, which tasks are to accurately recognize 3D objects around the vehicle, such as cars, pedestrians, and bicycles. Current research is primarily focused on successfully integrating data from camera and LiDAR sensors to enhance detection accuracy and reliability, while overcoming the limits of using a single sensor. In this paper, we provide a review of 3D object detection methods for multi-sensor fusion. First, we introduce common camera and LiDAR sensors and their data processing methods. Subsequently, we classify the fusion algorithms into three categories: input fusion, feature fusion, and late fusion, based on different fusion strategies, and conduct an in-depth survey and discussion on them to analyze their respective advantages and disadvantages. In addition, we provide an overview of public datasets commonly used in 3D object detection. Finally, we provide an outlook on the future direction of multi-sensor fusion 3D object detection technology.
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