基于注意力机制的3D车辆检测算法

3D车辆检测是自动驾驶场景中的一个关键问题，涉及到3D目标检测与目标分类。目前的3D检测与分类网络对于所有输入的点云数据一视同仁，但在实际检测过程中，点云中不同点对于检测的重要程度可能并不相同。为了得到更好的检测结果，通过引入注意力机制来得到不同点的特征的权重，从而在回归时让部分点的特征得到更多的重视。实验表明，该算法在保证实时效率的前提下，与现有算法相比，具有更高的准确度。

A 3D vehicle detection algorithm based on attention mechanism

3D vehicle detection is a key problem in automatic driving scene,which involves 3D object detection and 3D object classification.Current 3D detection and classification networks treat all input point cloud data equally.However,in the actual detection process,the importance of different points in the point cloud for detection may not be the same.In order to get better detection results,attention mechanism is introduced to get the weights of the features of different points,so that the features of some points can get more attentions in regression.Experiments show that the model has higher accuracy than the existing methods while maintaining real-time efficiency.