基于原始点云网格自注意力机制的三维目标检测方法

Grid self-attention mechanism 3D object detection method based on raw point cloud

为了增强感兴趣区域(RoI)的特征表达,包括空间网格特征编码模块和软回归损失,提出了一种基于原始点云网格自注意力机制的三维目标检测方法GT3D。网格特征编码模块用于通过自注意力机制对点的局部特征和空间特征进行有效加权,充分考虑点云之间的几何关系,以提供更准确的特征表达;软回归损失用于改善数据标注过程中由于标注不准确而产生的回归歧义问题。将所提方法在公开的三维目标检测数据集KITTI上进行实验。结果表明,所提方法相比其他已公开的基于点云的三维目标检测方法检测准确率提升明显,并提交了KITTI官方测试集进行公开测试,对简单、中等和困难3个难度等级的汽车检测准确率分别达到91.45%、82.76%和79.74%。

To enhance the feature representation of region of interest(RoI),which incorporated a spatial context encoding module and soft regression loss,a grid self-attention mechanism 3D object detection method based on raw point cloud,named GT3D,was proposed.The spatial context encoding module was designed to effectively weight the local and spatial features of points through the attention mechanism,considering the contribution of different point cloud features for a more accurate feature representation.The soft regression loss was introduced to address label ambiguity arising during the data annotation phase.Experiments conducted on the public KITTI 3D object detection dataset demonstrate that the proposed method achieves significant improvements in detection accuracy compared to other publicly available point cloud-based 3D object detection methods.The detection results of the test set are submitted to the official KITTI server for public evaluation,achieving detection accuracies of 91.45%,82.76%,and 79.74%for easy,moderate,and hard difficulty levels in car detection,respectively.